Data Science in the Medical Field
- 1st Edition - September 25, 2024
- Authors: Seifedine Kadry, Shubham Mahajan
- Language: English
- Paperback ISBN:9 7 8 - 0 - 4 4 3 - 2 4 0 2 8 - 7
- eBook ISBN:9 7 8 - 0 - 4 4 3 - 2 4 0 2 9 - 4
Data science has the potential to influence and improve fundamental services such as the healthcare sector. This book recognizes this fact by analyzing the potential uses of da… Read more
Data science has the potential to influence and improve fundamental services such as the healthcare sector. This book recognizes this fact by analyzing the potential uses of data science in healthcare. Every human body produces 2 TB of data each day. This information covers brain activity, stress level, heart rate, blood sugar level, and many other things. More sophisticated technology, such as data science, allows clinicians and researchers to handle such a massive volume of data to track the health of patients. The book focuses on the potential and the tools of data science to identify the signs of illness at an extremely early stage.
- Shows how improving automated analytical techniques can be used to generate new information from data for healthcare applications
- Combines a number of related fields, with a particular emphasis on machine learning, big data analytics, statistics, pattern recognition, computer vision, and semantic web technologies
- Provides information on the cutting-edge data science tools required to accelerate innovation for healthcare organizations and patients by reading this book
Researchers working in the healthcare or medical sectors- clinicians, academics and in industrial R&D. Final year undergraduate and postgraduate students
1. PPH 4.0: a privacy-preserving health 4.0 framework with machine learning and cellular automata
Arnab Mitra and Anabik Pal
1.1 Introduction
1.2 A brief survey of related technologies and past important works
1.2.1 Machine learning
1.2.2 MapReduce
1.2.3 Attribute-based encryption
1.2.4 Cellular automata
1.2.5 Elementary cellular automata alignment
1.2.6 Elementary cellular automata rules
1.3 Research methodology and proposed framework
1.3.1 Supervised approach for data dimensionality reduction
1.3.2 Filter methods
1.3.3 Correlation coefficient
1.3.4 Information gain
1.3.5 Fisher score
1.3.6 Mutual information
1.3.7 Chi-square
1.3.8 Wrapper methods
1.3.9 Forward selection approach
1.3.10 Backward elimination approach
1.3.11 Embedded methods
1.3.12 Feature transformation
1.3.13 Linear discriminant analysis
1.3.14 Principal component analysis
1.3.15 Autoencoder
1.3.16 Metric learning
1.4 Discussions
1.4.1 Integration of methods and disciplines to pursue the objectives
1.5 Conclusions and future research direction
Acknowledgment
Declaration of competing interest
References
2. An automatic detection and severity levels of COVID-19 using convolutional neural network models
Samba Siva Krishna Assish Yellepeddi and P. Kuppusamy
2.1 Introduction
2.2 Related work
2.3 Early diagnosis by deep learning strategies
2.4 Methodology
2.4.1 Preprocessing
2.4.2 Data augmentation
2.4.3 Transfer learning
2.4.4 Segmentation using the U-Net architecture
2.4.5 Classification using ReseNet50 or VGG16
2.5 Dataset and implementation
2.6 Performance evaluation metrics
2.6.1 Accuracy
2.6.2 Sensitivity
2.6.3 Precision
2.6.4 F-score
2.7 Comparison
2.8 Conclusion
References
3. Biosensors and disease diagnostics in medical field
Harpreet Kaur Channi, Ramandeep Sandhu, Deepika Ghai, Kanav Dhir, Komal Arora and Suman Lata Tripathi
3.1 Introduction
3.1.1 Importance of disease diagnostics in healthcare
3.1.2 Role of biosensors in disease diagnosis
3.2 Principle of biosensor
3.3 Architecture of biosensor structure
3.4 Different types of medical sensors
3.5 Biosensor technologies in disease diagnosis
3.6 Application of biosensors
3.6.1 Infectious diseases
3.6.2 Cancer
3.6.3 Diabetes
3.6.4 Cardiovascular diseases
3.6.5 Neurological disorders
3.6.6 Autoimmune diseases
3.7 Challenges and limitations of biosensors in disease diagnostics
3.8 Future perspectives and advancements in biosensor technology
3.8.1 Integration of biosensor with artificial intelligence and machine learning
3.8.2 Wearable biosensors and remote monitoring
3.9 Commercial and clinical adoption of biosensors
3.9.1 Current market landscape of biosensors in the medical field
3.9.2 Challenges in commercialization and widespread adoption
3.10 Case studies of biosensor applications
3.11 Conclusion
3.11.1 Future implications of biosensors in healthcare and medical field
References
4. Brain tumor recognition and classification techniques
Roaa Soloh, Ali Rammal and Mohamad El-Abed
4.1 Introduction
4.2 Imaging methods
4.3 Brain tumor detection, segmentation, and classification
4.3.1 Brain tumors detection and segmentation
4.4 Conclusion and discussion for segmentation
4.4.1 Brain tumor classification
4.5 Analysis study
4.6 Conclusion and discussion for classification
4.7 Conclusion and future prospects
References
5. Identifying the features and attributes of various artificial intelligence-based healthcare models
Nisha Soms, David Samuel Azariya S. and Abhinaya Saravanan A.
5.1 Introduction
5.2 Predictive analytics models
5.2.1 IBM Watson Health
5.2.2 Google DeepMind Health
5.2.3 Ayasdi
5.2.4 Cerner
5.2.5 Epic systems
5.2.6 Statistical Analysis System
5.3 Natural Language Processing models
5.3.1 BioBERT
5.3.2 ClinicalBERT
5.3.3 MedBERT
5.3.4 BlueBERT
5.3.5 PubmedBERT
5.3.6 Clinical BERT-based question answering
5.3.7 MedCAT
5.3.8 i2b2 Natural Language Processing framework
5.4 Chatbot models
5.4.1 Microsoft healthcare bot
5.4.2 Woebot
5.4.3 Your.MD
5.4.4 Buoy health
5.4.5 Babylon health
5.4.6 Infermedica
5.4.7 Ada Health
5.5 Computer Vision models
5.5.1 Mammography computer-aided detection systems
5.5.2 Retinal fundus imaging
5.5.3 Histopathology image analysis
5.5.4 Skin lesion analysis
5.5.5 Analysis of radiology imaging
5.5.6 Surgical vision systems
5.6 Conclusion
5.7 Artificial intelligence disclosure
References
6. Classification algorithms and optimization techniques in healthcare systems representation of dataset in medical applications
P. Deivendran, S. Muthukaruppasamy, G. Arun Sampaul Thomas and K. Saravanan
6.1 Introduction
6.2 Related works
6.3 Types of classification
6.4 Architecture and methods
6.5 Proposed attributes and methods
6.6 Efficiency and performance
6.7 Experiential result discussion
6.8 Conclusion
References
7. A knowledge discovery framework for COVID-19 disease from PubMed abstract using association rule hypergraph
Pradeepa Sampath, Vimal Shanmuganathan, Janmenjoy Nayak, Subbulakshmi Pasupathi, Prasun Chakrabarti and Kaliappan Madasamy
7.1 Introduction
7.2 Related works
7.3 Methodology
7.3.1 Data gathering and preprocessing
7.3.2 Keyword extraction using latent Dirichlet allocation with affinity propagation clustering
7.3.3 Generation of the association using affinity propagation-hypergraph
7.4 Experimental analysis
7.4.1 Data gathering and preprocessing
7.4.2 TF-IDF estimation
7.4.3 Latent Dirichlet allocation with affinity propagation
7.4.4 Extracted association
7.4.5 Comparative analysis
7.5 Conclusion
Author contribution
Acknowledgment
References
8. Predictive analysis in healthcare using data science: leveraging big data for improved patient care
Hirak Mazumdar and Kamil Reza Khondakar
8.1 Introduction
8.2 Data science in healthcare: an overview
8.2.1 Role of data science in healthcare transformation
8.2.2 Healthcare data science: progress challenges and opportunities
8.3 Predictive analysis techniques in healthcare
8.3.1 Data collection and reprocessing
8.3.2 Feature selection and engineering
8.3.3 Predictive analysis using machine learning models
8.3.4 Predictive model evaluation and validation
8.4 Application of predictive analysis in healthcare
8.4.1 Early disease detection and diagnosis
8.4.2 Personalized treatment planning
8.4.3 Hospital resource management and patient flow optimization
8.4.4 Public health surveillance and outbreak prediction
8.5 Case studies and success stories
8.5.1 Case study: IBM Watson health and chronic disease management
8.5.2 Case study: Pfizer’s predictive analytics for adverse drug reactions
8.5.3 Case study: partners healthcare and hospital readmission reduction
8.6 Conclusion and future research direction
References
9. Data science in medical field: advantages, challenges, and opportunities
S. Geetha, J. Madhusudanan and V. Prasanna Venkatesan
9.1 Introduction
9.2 Literature review
9.3 Overview of data science in medical field
9.4 Applications of data science in medical field
9.4.1 Predictive analytics
9.4.2 Diagnostics tools
9.4.3 Pharmaceutical services
9.4.4 Drug discovery and development
9.4.5 Healthcare resource optimization
9.4.6 Disease surveillance and outbreak prediction
9.4.7 Continuous monitoring and remote patient care
9.5 Advantages of data science in healthcare sector
9.6 Challenges of data science in healthcare sector
9.6.1 Data management
9.6.2 Privacy and security
9.6.3 Data retention
9.6.4 Maintaining cybersecurity
9.7 Opportunities of data science in healthcare sector
9.8 Discussion and future directions
9.9 Conclusion
Further reading
10. Decentralizing healthcare through parallel blockchain architecture: transmitting internet of medical things data through smart contracts in telecare medical information systems
Sebastian Melbye and Sahar Yassine
10.1 Introduction
10.2 Literature review
10.2.1 Telecare medical information systems
10.2.2 Blockchain technology
10.2.3 Internet of medical things
10.2.4 Patientdoctor parallel-chain communication
10.3 Network architecture and implementation
10.3.1 Parallel blockchain architecture
10.3.2 Communication layer
10.4 Application development and smart-contract deployment
10.4.1 Application structure
10.4.2 Smart-contract deployment
10.4.3 User interface/user experience
10.5 Results and discussion
10.6 Conclusion
10.7 Future work
References
11. Machine learning in heart disease prediction
Delshi Howsalya Devi R., R. Praveen and A. Asis Jovin
11.1 Introduction
11.2 Literature review
11.3 Proposed method
11.3.1 Random forest
11.3.2 Supporting vector machine
11.3.3 Artificial neural networks
11.4 Methodology
11.4.1 Data collection
11.4.2 Data exploration
11.4.3 Data set collection
11.4.4 Attribute selection
11.4.5 Data preprocessing
11.4.6 Balancing of data
11.4.7 Disease prediction
11.5 Software requirement
11.5.1 Anaconda
11.5.2 Python
11.5.3 Numpy
11.5.4 Pandas
11.5.5 Sklearn
11.5.6 Tensorflow
11.5.7 Objective and types of testing
11.6 Conclusion
References
12. U-Net-based approaches for brain tumor segmentation
Vegard Eikenes and Seifedine Kadry
12.1 Introduction
12.2 Brain tumors
12.3 Magnetic resonance imaging
12.3.1 Radiologists’ role in magnetic resonance imaging image analysis
12.4 Deep learning
12.5 Convolutional neural networks
12.6 U-Net
12.7 Summary of related work
12.7.1 Methodology
12.8 Experimental setup
12.8.1 Process
12.8.2 Data preprocessing
12.9 Model building and training
12.9.1 Data split
12.9.2 Performance evaluation
12.9.3 Implementation and results
12.10 2D U-Net architecture
12.11 2D Modalities results
12.11.1 Optimization algorithm results
12.11.2 Activation function results
12.11.3 Normalization and dropout results
12.12 3D U-Net architecture
12.13 3D modalities results
12.13.1 Normalization and dropout results
12.14 Residual U-Net architecture
12.15 Activation function results
12.16 Normalization and dropout results
12.17 Attention U-Net architecture
12.18 Normalization and dropout results
12.19 Residual attention U-Net architecture
12.20 Normalization and dropout results
12.21 Architecture comparison
12.22 Conclusion
12.23 Research contribution
12.24 Future work
References
13. Explainable image recognition models for aiding radiologists in clinical decision making
Auxilia Michael, Abarna Vasanth, Feron Arockiam Sagayaradjy, Mohammed Feroz and Rahul Gnanapragasam
13.1 Introduction
13.2 Literature review
13.3 Proposed work
13.3.1 Data gathering and preparation
13.3.2 Annotation of abnormal regions or abnormalities in the dataset
13.3.3 Preprocessing steps for image enhancement and normalization
13.3.4 Training an abnormality detection model
13.3.5 Abnormality detection and localization
13.4 X-ray
13.5 Magnetic resonance imaging scan
13.5.1 Assessment of the performance of abnormality detection for each scan type
13.5.2 Extraction of abnormality information
13.5.3 Text generation for abnormality narration
13.5.4 Presentation of abnormality narration to the user
13.6 Experimental results
13.6.1 Performance metrics
13.6.2 4.3 Text generation metrics
13.6.3 4.4 Comparison with existing methods
13.7 Concluding remarks and prospects
References
14. Prediction of heart failure disease using classification algorithms along with performance parameters
Karthika Natarajan and C. Rajeev
14.1 Introduction
14.2 Related work
14.3 Methodology
14.3.1 Data preprocessing
14.3.2 Feature engineering
14.3.3 Feature selection
14.3.4 Traintest split
14.3.5 Machine learning models
14.3.6 Performance parameters
14.3.7 Results and discussion
14.4 Conclusion
References
15. Cancer survival prediction using artificial intelligence: current status and future prospects
Hasan Shaikh and Rashid Ali
15.1 Introduction
15.2 Literature review
15.2.1 Classical machine learning techniques for cancer survival prediction
15.2.2 Ensemble learning techniques for cancer survival prediction
15.2.3 Deep learning techniques for cancer survival prediction
15.3 Evaluation metrics for cancer survival prediction
15.3.1 Classification metrics
15.3.2 Discriminative metrics
15.3.3 Explainability metrics
15.4 Challenges and limitations of using artificial intelligence techniques
15.4.1 Data availability and quality (the data dilemma)
15.4.2 Interpretation and explainability (the artificial intelligence enigma)
15.4.3 Ethical considerations (guardian of privacy)
15.5 Conclusion and future direction
References
16. Heart disease prediction in pregnant women with diabetes using machine learning
Sujatha Rajkumar, Svetlana Stanarevic, Yogeshwar P, Karthikeyan BM and Kaviya V
16.1 Introduction
16.2 Literature review
16.3 Proposed research work
16.3.1 Comprehensive guide to predictive modeling and machine learning
16.3.2 System flow diagram for advanced heart disease prediction in diabetic pregnancy
16.3.3 Potential benefits on early risk prediction during diabetic pregnancy
16.3.4 Advanced machine learning approaches for early detection and risk assessment in diabetic pregnancy
16.4 Results and discussion
16.5 Performance metrics for machine learning models: logistic regression, random forest, and decision tree
16.5.1 Novelty of proposed work
16.6 Conclusion
16.7 Future scope
AI disclosure
References
17. Healthcare using image recognition technology
Karthika Natarajan and SivaTejaswi Jonna
17.1 Introduction
17.1.1 What is image processing, exactly?
17.1.2 What does medical image processing entail?
17.1.3 What is image classification?
17.1.4 How does image classification work?
17.1.5 What is image processing in medicine?
17.1.6 How does medical image processing work?
17.2 What is machine learning and how does it work?
17.2.1 Exactly what is machine learning?
17.2.2 What is the process of machine learning?
17.2.3 What kinds of machine learning are there?
17.2.4 What is the importance of machine learning?
17.2.5 Machine learning’s principal uses
17.3 Master’s in healthcare
17.3.1 Applications of artificial intelligence in healthcare
17.4 Discussion on medical image processing
17.4.1 Related resources
17.5 Conclusion
References
18. Integration of deep learning and blockchain technology for a smart healthcare record management system
Sujatha Rajkumar, Vandana Mansur, Akshat, Yashraj Motwani, Vinod Salunkhe and Thomas M. Chen
18.1 Introduction
18.2 Importance of smart healthcare
18.2.1 Internet of Medical Things
18.2.2 Smart e-healthcare
18.3 Emerging technologies in Internet of Medical Things
18.3.1 Artificial intelligence in Internet of Medical Things
18.3.2 Blockchain in Internet of Medical Things
18.3.3 Machine learning in Internet of Medical Things
18.3.4 Cloud computing in Internet of Medical Things
18.4 Digital twins, telemedicine, and metaverse in Internet of Medical Things
18.5 Case study: patient centric healthcare model
18.5.1 Healthcare data analysis using deep learning-based segmentation and classification model
18.5.2 Blockchain-based electronic health record for medical record data storage
18.6 Results
18.6.1 Classification model performance of medical images
18.6.2 Security measures on Ethereum blockchain-based attacks
18.6.3 Ethereum blockchain evaluation metrics
18.7 Discussion
18.8 Conclusion
References
19. Internet of things based smart health and attendance monitoring system in an institution for COVID-19
C.M. Arun Kumar, Senthilkumar Subramaniyan and C. Kavitha
19.1 Introduction
19.2 Coronavirus
19.2.1 Smart healthcare services
19.2.2 Proposed system design
19.3 Different technologies
19.3.1 Evolution of Internet of Things
19.3.2 Block diagram
19.3.3 Mask detection unit
19.4 Result and discussion
19.4.1 Mask detection
19.4.2 Attendance report
19.5 Conclusions
References
20. Medical diagnosis using image processing techniques
Aavampreet Kour
20.1 Introduction
20.2 Image processing techniques in medical diagnosis
20.2.1 Image acquisition
20.2.2 Preprocessing
20.2.3 Feature extraction
20.2.4 Classification
20.3 Recent advancements in medical diagnosis
20.3.1 Computer-aided diagnosis systems
20.3.2 Deep learning-based approach
20.3.3 Transfer learning
20.3.4 Multimodal fusion techniques
20.3.5 Real-time diagnosis
20.4 Methodology and applications
20.4.1 Chest X-ray analysis for pneumonia detection
20.4.2 Mammography interpretation through image processing
20.5 Advancements in image processing
20.5.1 Improved accuracy
20.5.2 Faster diagnosis
20.5.3 Enhanced visualization
20.5.4 Remote diagnoses and telemedicine
20.5.5 Integration with artificial intelligence and machine learning
20.6 Challenges in medical diagnosis using image processing
20.6.1 Data quality and availability
20.6.2 Interpretability and explainability
20.6.3 Limited labeled data
20.6.4 Integration of various imaging modalities
20.6.5 Real-time processing and computational efficiency
20.7 Potential solutions and future directions
20.7.1 Data synthesis and augmentation methods
20.7.2 Explainable artificial intelligence methodologies
20.7.3 Active and semisupervised learning
20.7.4 Multimodal and hybrid approaches
20.7.5 Edge computing and hardware optimization
20.8 Evaluation metrics and performance analysis
20.8.1 Accuracy
20.8.2 Precision
20.8.3 Sensitivity and specificity
20.8.4 F1 score
20.8.5 Analysis of the receiver operating characteristic curve
20.8.6 Performance evaluation for localization and segmentation
20.9 Conclusion
References
21. Harnessing the potential of predictive analytics and machine learning in healthcare: empowering clinical research and patient care
G. Arun Sampaul Thomas, S. Muthukaruppasamy, P. Deivendran, G. Sudha and K. Saravanan
21.1 Introduction
21.1.1 Uses of predictive analytics in healthcare
21.1.2 Benefits of predictive analytics in healthcare
21.2 Healthcare predictive modeling
21.3 The use of machine learning in the medical business
21.4 Healthcare predictive analytics example
21.4.1 Examples of predictive analytics used in the healthcare industry
21.4.2 Obstacles faced by artificial intelligence and machine learning in the healthcare industry
21.4.3 Possible answers to frequent challenges in the healthcare industry
21.5 The use of predictive analytics with the use of machine learning
21.5.1 Examples of learning machines in action
21.6 Conclusion
References
22. Predictive analysis in healthcare using data science
C. Aarthy, D. Balakrishnan and Nandhagopal Subramani
22.1 Introduction
22.2 Related works
22.3 An in-depth look of data science
22.3.1 Data science on practice
22.4 Data science in the world of healthcare
22.4.1 Breast cancer
22.4.2 Selection of features
22.4.3 Initial data visualization
22.4.4 The approach of random forests to forecasting data
22.4.5 Added potential elements
22.4.6 Interpreting signals related to medicine
22.4.7 Management of patient data
22.4.8 Medical data privacy and fraud prevention
22.5 The healthcare sector
22.5.1 Expanding the analytics infrastructure
22.5.2 Using cutting-edge analytics to forecast results
22.5.3 Utilizing machine learning to analyze patient data
22.5.4 Big data management
22.5.5 Incorporating information technology into healthcare
22.6 Strategies and tools for using data science in healthcare
22.6.1 Machine learning
22.6.2 Deep learning
22.6.3 Natural language processing
22.6.4 Predictive analytics
22.6.5 Data mining
22.6.6 Big data technologies
22.6.7 Electronic health records
22.6.8 Telemedicine and remote healthcare
22.6.9 Health informatics
22.6.10 Applications for mobile Health
22.6.11 Decision support systems
22.7 A guide to data science in healthcare: applications
22.7.1 Data science for medical imaging
22.7.2 Data science for genomics
22.7.3 Data science in drug discovery
22.7.4 Predictive data analytics in healthcare
22.7.5 Monitoring patient health and data science
22.7.6 Tracking and preventing diseases with data science
22.7.7 Virtual assistance with data science
22.8 Data science’s effects on healthcare
22.8.1 Patent foramen ovale
22.8.2 Diagnostic advancements
22.8.3 Improved long-term results
22.8.4 Patient risk stratification
22.9 Healthcare benefits of data science
22.10 Challenges
22.11 Healthcare data science future
22.12 Conclusion
References
23. Recommender systems in healthcare—an emerging technology
Kusumalatha Karre and Ramadevi Y.
23.1 Introduction
23.1.1 Recommender system with various filtering techniques
23.2 Recommender Systems in Hhealthcare
23.2.1 Examples of Recommender systems in healthcare industry
23.3 Major challenges in Rrecommender Ssystems
23.3.1 Data collection
23.3.2 Evaluation metrics
23.3.3 Privacy
23.4 Conclusion
References
24. Robotics: challenges and opportunities in healthcare
Ruby Pant, Kapil Joshi and Shubham Mahajan
24.1 Introduction
24.2 Research method
24.2.1 History and overview of robot
24.3 Literature survey
24.4 Advantages of robot in healthcare sectors
24.5 Applications of robotics in healthcare
24.6 Challenges in implementing robotics in healthcare
24.7 Conclusion
References
25. A new era of the healthcare industry using Internet of Medical Things
Hamnah Rao, Parul Agarwal, Saima Naaz, Sapna Jain and Ahmed Obaid
25.1 Introduction
25.2 Structure of Internet of Things-based healthcare system
25.3 Literature review
25.4 Types of Internet of Medical Things devices
25.5 Components of Internet of Medical Things
25.6 Benefits of Internet of Medical Things
25.7 Challenges of Internet of Medical Things
25.7.1 Technical challenges
25.7.2 Financial challenges
25.7.3 Ethical challenges
25.8 Conclusion and future work
References
26. Single cell genomics unleashed: exploring the landscape of endometriosis with machine learning, gene expression profiling, and therapeutic target discovery
Sudip Mondal
26.1 Introduction
26.2 Advancement of machine learning classifiers for the study of endometriosis
26.3 Single-cell analysis of endometriosis
26.4 Gene expression analysis of endometrium
26.5 Identification of novel drug targets for endometriosis
26.6 Discussion
Acknowledgments
References
27. Analyzing the success of the thriving machine prediction model for Parkinson’s disease prognosis: a comprehensive review
Marion O. Adebiyi, Prisca O. Olawoye and Moses Abiodun
27.1 Introduction
27.2 Related works
27.3 Methods
27.3.1 Principal component analysis
27.3.2 Independent component analysis
27.3.3 Linear discriminant analysis
27.3.4 t-Distributed stochastic neighbor embedding
27.3.5 Nonnegative matrix factorization
27.3.6 Recursive feature elimination
27.3.7 SelectKBest
27.3.8 Minimum redundancy maximum relevance
27.3.9 Least absolute shrinkage and selection operator
27.3.10 Support vector machines
27.3.11 Random forest
27.3.12 K-nearest neighbor
27.3.13 Decision tree
27.3.14 Artificial neural network
27.3.15 Convolutional neural networks
27.4 Discussions
27.5 Conclusion
References
Arnab Mitra and Anabik Pal
1.1 Introduction
1.2 A brief survey of related technologies and past important works
1.2.1 Machine learning
1.2.2 MapReduce
1.2.3 Attribute-based encryption
1.2.4 Cellular automata
1.2.5 Elementary cellular automata alignment
1.2.6 Elementary cellular automata rules
1.3 Research methodology and proposed framework
1.3.1 Supervised approach for data dimensionality reduction
1.3.2 Filter methods
1.3.3 Correlation coefficient
1.3.4 Information gain
1.3.5 Fisher score
1.3.6 Mutual information
1.3.7 Chi-square
1.3.8 Wrapper methods
1.3.9 Forward selection approach
1.3.10 Backward elimination approach
1.3.11 Embedded methods
1.3.12 Feature transformation
1.3.13 Linear discriminant analysis
1.3.14 Principal component analysis
1.3.15 Autoencoder
1.3.16 Metric learning
1.4 Discussions
1.4.1 Integration of methods and disciplines to pursue the objectives
1.5 Conclusions and future research direction
Acknowledgment
Declaration of competing interest
References
2. An automatic detection and severity levels of COVID-19 using convolutional neural network models
Samba Siva Krishna Assish Yellepeddi and P. Kuppusamy
2.1 Introduction
2.2 Related work
2.3 Early diagnosis by deep learning strategies
2.4 Methodology
2.4.1 Preprocessing
2.4.2 Data augmentation
2.4.3 Transfer learning
2.4.4 Segmentation using the U-Net architecture
2.4.5 Classification using ReseNet50 or VGG16
2.5 Dataset and implementation
2.6 Performance evaluation metrics
2.6.1 Accuracy
2.6.2 Sensitivity
2.6.3 Precision
2.6.4 F-score
2.7 Comparison
2.8 Conclusion
References
3. Biosensors and disease diagnostics in medical field
Harpreet Kaur Channi, Ramandeep Sandhu, Deepika Ghai, Kanav Dhir, Komal Arora and Suman Lata Tripathi
3.1 Introduction
3.1.1 Importance of disease diagnostics in healthcare
3.1.2 Role of biosensors in disease diagnosis
3.2 Principle of biosensor
3.3 Architecture of biosensor structure
3.4 Different types of medical sensors
3.5 Biosensor technologies in disease diagnosis
3.6 Application of biosensors
3.6.1 Infectious diseases
3.6.2 Cancer
3.6.3 Diabetes
3.6.4 Cardiovascular diseases
3.6.5 Neurological disorders
3.6.6 Autoimmune diseases
3.7 Challenges and limitations of biosensors in disease diagnostics
3.8 Future perspectives and advancements in biosensor technology
3.8.1 Integration of biosensor with artificial intelligence and machine learning
3.8.2 Wearable biosensors and remote monitoring
3.9 Commercial and clinical adoption of biosensors
3.9.1 Current market landscape of biosensors in the medical field
3.9.2 Challenges in commercialization and widespread adoption
3.10 Case studies of biosensor applications
3.11 Conclusion
3.11.1 Future implications of biosensors in healthcare and medical field
References
4. Brain tumor recognition and classification techniques
Roaa Soloh, Ali Rammal and Mohamad El-Abed
4.1 Introduction
4.2 Imaging methods
4.3 Brain tumor detection, segmentation, and classification
4.3.1 Brain tumors detection and segmentation
4.4 Conclusion and discussion for segmentation
4.4.1 Brain tumor classification
4.5 Analysis study
4.6 Conclusion and discussion for classification
4.7 Conclusion and future prospects
References
5. Identifying the features and attributes of various artificial intelligence-based healthcare models
Nisha Soms, David Samuel Azariya S. and Abhinaya Saravanan A.
5.1 Introduction
5.2 Predictive analytics models
5.2.1 IBM Watson Health
5.2.2 Google DeepMind Health
5.2.3 Ayasdi
5.2.4 Cerner
5.2.5 Epic systems
5.2.6 Statistical Analysis System
5.3 Natural Language Processing models
5.3.1 BioBERT
5.3.2 ClinicalBERT
5.3.3 MedBERT
5.3.4 BlueBERT
5.3.5 PubmedBERT
5.3.6 Clinical BERT-based question answering
5.3.7 MedCAT
5.3.8 i2b2 Natural Language Processing framework
5.4 Chatbot models
5.4.1 Microsoft healthcare bot
5.4.2 Woebot
5.4.3 Your.MD
5.4.4 Buoy health
5.4.5 Babylon health
5.4.6 Infermedica
5.4.7 Ada Health
5.5 Computer Vision models
5.5.1 Mammography computer-aided detection systems
5.5.2 Retinal fundus imaging
5.5.3 Histopathology image analysis
5.5.4 Skin lesion analysis
5.5.5 Analysis of radiology imaging
5.5.6 Surgical vision systems
5.6 Conclusion
5.7 Artificial intelligence disclosure
References
6. Classification algorithms and optimization techniques in healthcare systems representation of dataset in medical applications
P. Deivendran, S. Muthukaruppasamy, G. Arun Sampaul Thomas and K. Saravanan
6.1 Introduction
6.2 Related works
6.3 Types of classification
6.4 Architecture and methods
6.5 Proposed attributes and methods
6.6 Efficiency and performance
6.7 Experiential result discussion
6.8 Conclusion
References
7. A knowledge discovery framework for COVID-19 disease from PubMed abstract using association rule hypergraph
Pradeepa Sampath, Vimal Shanmuganathan, Janmenjoy Nayak, Subbulakshmi Pasupathi, Prasun Chakrabarti and Kaliappan Madasamy
7.1 Introduction
7.2 Related works
7.3 Methodology
7.3.1 Data gathering and preprocessing
7.3.2 Keyword extraction using latent Dirichlet allocation with affinity propagation clustering
7.3.3 Generation of the association using affinity propagation-hypergraph
7.4 Experimental analysis
7.4.1 Data gathering and preprocessing
7.4.2 TF-IDF estimation
7.4.3 Latent Dirichlet allocation with affinity propagation
7.4.4 Extracted association
7.4.5 Comparative analysis
7.5 Conclusion
Author contribution
Acknowledgment
References
8. Predictive analysis in healthcare using data science: leveraging big data for improved patient care
Hirak Mazumdar and Kamil Reza Khondakar
8.1 Introduction
8.2 Data science in healthcare: an overview
8.2.1 Role of data science in healthcare transformation
8.2.2 Healthcare data science: progress challenges and opportunities
8.3 Predictive analysis techniques in healthcare
8.3.1 Data collection and reprocessing
8.3.2 Feature selection and engineering
8.3.3 Predictive analysis using machine learning models
8.3.4 Predictive model evaluation and validation
8.4 Application of predictive analysis in healthcare
8.4.1 Early disease detection and diagnosis
8.4.2 Personalized treatment planning
8.4.3 Hospital resource management and patient flow optimization
8.4.4 Public health surveillance and outbreak prediction
8.5 Case studies and success stories
8.5.1 Case study: IBM Watson health and chronic disease management
8.5.2 Case study: Pfizer’s predictive analytics for adverse drug reactions
8.5.3 Case study: partners healthcare and hospital readmission reduction
8.6 Conclusion and future research direction
References
9. Data science in medical field: advantages, challenges, and opportunities
S. Geetha, J. Madhusudanan and V. Prasanna Venkatesan
9.1 Introduction
9.2 Literature review
9.3 Overview of data science in medical field
9.4 Applications of data science in medical field
9.4.1 Predictive analytics
9.4.2 Diagnostics tools
9.4.3 Pharmaceutical services
9.4.4 Drug discovery and development
9.4.5 Healthcare resource optimization
9.4.6 Disease surveillance and outbreak prediction
9.4.7 Continuous monitoring and remote patient care
9.5 Advantages of data science in healthcare sector
9.6 Challenges of data science in healthcare sector
9.6.1 Data management
9.6.2 Privacy and security
9.6.3 Data retention
9.6.4 Maintaining cybersecurity
9.7 Opportunities of data science in healthcare sector
9.8 Discussion and future directions
9.9 Conclusion
Further reading
10. Decentralizing healthcare through parallel blockchain architecture: transmitting internet of medical things data through smart contracts in telecare medical information systems
Sebastian Melbye and Sahar Yassine
10.1 Introduction
10.2 Literature review
10.2.1 Telecare medical information systems
10.2.2 Blockchain technology
10.2.3 Internet of medical things
10.2.4 Patientdoctor parallel-chain communication
10.3 Network architecture and implementation
10.3.1 Parallel blockchain architecture
10.3.2 Communication layer
10.4 Application development and smart-contract deployment
10.4.1 Application structure
10.4.2 Smart-contract deployment
10.4.3 User interface/user experience
10.5 Results and discussion
10.6 Conclusion
10.7 Future work
References
11. Machine learning in heart disease prediction
Delshi Howsalya Devi R., R. Praveen and A. Asis Jovin
11.1 Introduction
11.2 Literature review
11.3 Proposed method
11.3.1 Random forest
11.3.2 Supporting vector machine
11.3.3 Artificial neural networks
11.4 Methodology
11.4.1 Data collection
11.4.2 Data exploration
11.4.3 Data set collection
11.4.4 Attribute selection
11.4.5 Data preprocessing
11.4.6 Balancing of data
11.4.7 Disease prediction
11.5 Software requirement
11.5.1 Anaconda
11.5.2 Python
11.5.3 Numpy
11.5.4 Pandas
11.5.5 Sklearn
11.5.6 Tensorflow
11.5.7 Objective and types of testing
11.6 Conclusion
References
12. U-Net-based approaches for brain tumor segmentation
Vegard Eikenes and Seifedine Kadry
12.1 Introduction
12.2 Brain tumors
12.3 Magnetic resonance imaging
12.3.1 Radiologists’ role in magnetic resonance imaging image analysis
12.4 Deep learning
12.5 Convolutional neural networks
12.6 U-Net
12.7 Summary of related work
12.7.1 Methodology
12.8 Experimental setup
12.8.1 Process
12.8.2 Data preprocessing
12.9 Model building and training
12.9.1 Data split
12.9.2 Performance evaluation
12.9.3 Implementation and results
12.10 2D U-Net architecture
12.11 2D Modalities results
12.11.1 Optimization algorithm results
12.11.2 Activation function results
12.11.3 Normalization and dropout results
12.12 3D U-Net architecture
12.13 3D modalities results
12.13.1 Normalization and dropout results
12.14 Residual U-Net architecture
12.15 Activation function results
12.16 Normalization and dropout results
12.17 Attention U-Net architecture
12.18 Normalization and dropout results
12.19 Residual attention U-Net architecture
12.20 Normalization and dropout results
12.21 Architecture comparison
12.22 Conclusion
12.23 Research contribution
12.24 Future work
References
13. Explainable image recognition models for aiding radiologists in clinical decision making
Auxilia Michael, Abarna Vasanth, Feron Arockiam Sagayaradjy, Mohammed Feroz and Rahul Gnanapragasam
13.1 Introduction
13.2 Literature review
13.3 Proposed work
13.3.1 Data gathering and preparation
13.3.2 Annotation of abnormal regions or abnormalities in the dataset
13.3.3 Preprocessing steps for image enhancement and normalization
13.3.4 Training an abnormality detection model
13.3.5 Abnormality detection and localization
13.4 X-ray
13.5 Magnetic resonance imaging scan
13.5.1 Assessment of the performance of abnormality detection for each scan type
13.5.2 Extraction of abnormality information
13.5.3 Text generation for abnormality narration
13.5.4 Presentation of abnormality narration to the user
13.6 Experimental results
13.6.1 Performance metrics
13.6.2 4.3 Text generation metrics
13.6.3 4.4 Comparison with existing methods
13.7 Concluding remarks and prospects
References
14. Prediction of heart failure disease using classification algorithms along with performance parameters
Karthika Natarajan and C. Rajeev
14.1 Introduction
14.2 Related work
14.3 Methodology
14.3.1 Data preprocessing
14.3.2 Feature engineering
14.3.3 Feature selection
14.3.4 Traintest split
14.3.5 Machine learning models
14.3.6 Performance parameters
14.3.7 Results and discussion
14.4 Conclusion
References
15. Cancer survival prediction using artificial intelligence: current status and future prospects
Hasan Shaikh and Rashid Ali
15.1 Introduction
15.2 Literature review
15.2.1 Classical machine learning techniques for cancer survival prediction
15.2.2 Ensemble learning techniques for cancer survival prediction
15.2.3 Deep learning techniques for cancer survival prediction
15.3 Evaluation metrics for cancer survival prediction
15.3.1 Classification metrics
15.3.2 Discriminative metrics
15.3.3 Explainability metrics
15.4 Challenges and limitations of using artificial intelligence techniques
15.4.1 Data availability and quality (the data dilemma)
15.4.2 Interpretation and explainability (the artificial intelligence enigma)
15.4.3 Ethical considerations (guardian of privacy)
15.5 Conclusion and future direction
References
16. Heart disease prediction in pregnant women with diabetes using machine learning
Sujatha Rajkumar, Svetlana Stanarevic, Yogeshwar P, Karthikeyan BM and Kaviya V
16.1 Introduction
16.2 Literature review
16.3 Proposed research work
16.3.1 Comprehensive guide to predictive modeling and machine learning
16.3.2 System flow diagram for advanced heart disease prediction in diabetic pregnancy
16.3.3 Potential benefits on early risk prediction during diabetic pregnancy
16.3.4 Advanced machine learning approaches for early detection and risk assessment in diabetic pregnancy
16.4 Results and discussion
16.5 Performance metrics for machine learning models: logistic regression, random forest, and decision tree
16.5.1 Novelty of proposed work
16.6 Conclusion
16.7 Future scope
AI disclosure
References
17. Healthcare using image recognition technology
Karthika Natarajan and SivaTejaswi Jonna
17.1 Introduction
17.1.1 What is image processing, exactly?
17.1.2 What does medical image processing entail?
17.1.3 What is image classification?
17.1.4 How does image classification work?
17.1.5 What is image processing in medicine?
17.1.6 How does medical image processing work?
17.2 What is machine learning and how does it work?
17.2.1 Exactly what is machine learning?
17.2.2 What is the process of machine learning?
17.2.3 What kinds of machine learning are there?
17.2.4 What is the importance of machine learning?
17.2.5 Machine learning’s principal uses
17.3 Master’s in healthcare
17.3.1 Applications of artificial intelligence in healthcare
17.4 Discussion on medical image processing
17.4.1 Related resources
17.5 Conclusion
References
18. Integration of deep learning and blockchain technology for a smart healthcare record management system
Sujatha Rajkumar, Vandana Mansur, Akshat, Yashraj Motwani, Vinod Salunkhe and Thomas M. Chen
18.1 Introduction
18.2 Importance of smart healthcare
18.2.1 Internet of Medical Things
18.2.2 Smart e-healthcare
18.3 Emerging technologies in Internet of Medical Things
18.3.1 Artificial intelligence in Internet of Medical Things
18.3.2 Blockchain in Internet of Medical Things
18.3.3 Machine learning in Internet of Medical Things
18.3.4 Cloud computing in Internet of Medical Things
18.4 Digital twins, telemedicine, and metaverse in Internet of Medical Things
18.5 Case study: patient centric healthcare model
18.5.1 Healthcare data analysis using deep learning-based segmentation and classification model
18.5.2 Blockchain-based electronic health record for medical record data storage
18.6 Results
18.6.1 Classification model performance of medical images
18.6.2 Security measures on Ethereum blockchain-based attacks
18.6.3 Ethereum blockchain evaluation metrics
18.7 Discussion
18.8 Conclusion
References
19. Internet of things based smart health and attendance monitoring system in an institution for COVID-19
C.M. Arun Kumar, Senthilkumar Subramaniyan and C. Kavitha
19.1 Introduction
19.2 Coronavirus
19.2.1 Smart healthcare services
19.2.2 Proposed system design
19.3 Different technologies
19.3.1 Evolution of Internet of Things
19.3.2 Block diagram
19.3.3 Mask detection unit
19.4 Result and discussion
19.4.1 Mask detection
19.4.2 Attendance report
19.5 Conclusions
References
20. Medical diagnosis using image processing techniques
Aavampreet Kour
20.1 Introduction
20.2 Image processing techniques in medical diagnosis
20.2.1 Image acquisition
20.2.2 Preprocessing
20.2.3 Feature extraction
20.2.4 Classification
20.3 Recent advancements in medical diagnosis
20.3.1 Computer-aided diagnosis systems
20.3.2 Deep learning-based approach
20.3.3 Transfer learning
20.3.4 Multimodal fusion techniques
20.3.5 Real-time diagnosis
20.4 Methodology and applications
20.4.1 Chest X-ray analysis for pneumonia detection
20.4.2 Mammography interpretation through image processing
20.5 Advancements in image processing
20.5.1 Improved accuracy
20.5.2 Faster diagnosis
20.5.3 Enhanced visualization
20.5.4 Remote diagnoses and telemedicine
20.5.5 Integration with artificial intelligence and machine learning
20.6 Challenges in medical diagnosis using image processing
20.6.1 Data quality and availability
20.6.2 Interpretability and explainability
20.6.3 Limited labeled data
20.6.4 Integration of various imaging modalities
20.6.5 Real-time processing and computational efficiency
20.7 Potential solutions and future directions
20.7.1 Data synthesis and augmentation methods
20.7.2 Explainable artificial intelligence methodologies
20.7.3 Active and semisupervised learning
20.7.4 Multimodal and hybrid approaches
20.7.5 Edge computing and hardware optimization
20.8 Evaluation metrics and performance analysis
20.8.1 Accuracy
20.8.2 Precision
20.8.3 Sensitivity and specificity
20.8.4 F1 score
20.8.5 Analysis of the receiver operating characteristic curve
20.8.6 Performance evaluation for localization and segmentation
20.9 Conclusion
References
21. Harnessing the potential of predictive analytics and machine learning in healthcare: empowering clinical research and patient care
G. Arun Sampaul Thomas, S. Muthukaruppasamy, P. Deivendran, G. Sudha and K. Saravanan
21.1 Introduction
21.1.1 Uses of predictive analytics in healthcare
21.1.2 Benefits of predictive analytics in healthcare
21.2 Healthcare predictive modeling
21.3 The use of machine learning in the medical business
21.4 Healthcare predictive analytics example
21.4.1 Examples of predictive analytics used in the healthcare industry
21.4.2 Obstacles faced by artificial intelligence and machine learning in the healthcare industry
21.4.3 Possible answers to frequent challenges in the healthcare industry
21.5 The use of predictive analytics with the use of machine learning
21.5.1 Examples of learning machines in action
21.6 Conclusion
References
22. Predictive analysis in healthcare using data science
C. Aarthy, D. Balakrishnan and Nandhagopal Subramani
22.1 Introduction
22.2 Related works
22.3 An in-depth look of data science
22.3.1 Data science on practice
22.4 Data science in the world of healthcare
22.4.1 Breast cancer
22.4.2 Selection of features
22.4.3 Initial data visualization
22.4.4 The approach of random forests to forecasting data
22.4.5 Added potential elements
22.4.6 Interpreting signals related to medicine
22.4.7 Management of patient data
22.4.8 Medical data privacy and fraud prevention
22.5 The healthcare sector
22.5.1 Expanding the analytics infrastructure
22.5.2 Using cutting-edge analytics to forecast results
22.5.3 Utilizing machine learning to analyze patient data
22.5.4 Big data management
22.5.5 Incorporating information technology into healthcare
22.6 Strategies and tools for using data science in healthcare
22.6.1 Machine learning
22.6.2 Deep learning
22.6.3 Natural language processing
22.6.4 Predictive analytics
22.6.5 Data mining
22.6.6 Big data technologies
22.6.7 Electronic health records
22.6.8 Telemedicine and remote healthcare
22.6.9 Health informatics
22.6.10 Applications for mobile Health
22.6.11 Decision support systems
22.7 A guide to data science in healthcare: applications
22.7.1 Data science for medical imaging
22.7.2 Data science for genomics
22.7.3 Data science in drug discovery
22.7.4 Predictive data analytics in healthcare
22.7.5 Monitoring patient health and data science
22.7.6 Tracking and preventing diseases with data science
22.7.7 Virtual assistance with data science
22.8 Data science’s effects on healthcare
22.8.1 Patent foramen ovale
22.8.2 Diagnostic advancements
22.8.3 Improved long-term results
22.8.4 Patient risk stratification
22.9 Healthcare benefits of data science
22.10 Challenges
22.11 Healthcare data science future
22.12 Conclusion
References
23. Recommender systems in healthcare—an emerging technology
Kusumalatha Karre and Ramadevi Y.
23.1 Introduction
23.1.1 Recommender system with various filtering techniques
23.2 Recommender Systems in Hhealthcare
23.2.1 Examples of Recommender systems in healthcare industry
23.3 Major challenges in Rrecommender Ssystems
23.3.1 Data collection
23.3.2 Evaluation metrics
23.3.3 Privacy
23.4 Conclusion
References
24. Robotics: challenges and opportunities in healthcare
Ruby Pant, Kapil Joshi and Shubham Mahajan
24.1 Introduction
24.2 Research method
24.2.1 History and overview of robot
24.3 Literature survey
24.4 Advantages of robot in healthcare sectors
24.5 Applications of robotics in healthcare
24.6 Challenges in implementing robotics in healthcare
24.7 Conclusion
References
25. A new era of the healthcare industry using Internet of Medical Things
Hamnah Rao, Parul Agarwal, Saima Naaz, Sapna Jain and Ahmed Obaid
25.1 Introduction
25.2 Structure of Internet of Things-based healthcare system
25.3 Literature review
25.4 Types of Internet of Medical Things devices
25.5 Components of Internet of Medical Things
25.6 Benefits of Internet of Medical Things
25.7 Challenges of Internet of Medical Things
25.7.1 Technical challenges
25.7.2 Financial challenges
25.7.3 Ethical challenges
25.8 Conclusion and future work
References
26. Single cell genomics unleashed: exploring the landscape of endometriosis with machine learning, gene expression profiling, and therapeutic target discovery
Sudip Mondal
26.1 Introduction
26.2 Advancement of machine learning classifiers for the study of endometriosis
26.3 Single-cell analysis of endometriosis
26.4 Gene expression analysis of endometrium
26.5 Identification of novel drug targets for endometriosis
26.6 Discussion
Acknowledgments
References
27. Analyzing the success of the thriving machine prediction model for Parkinson’s disease prognosis: a comprehensive review
Marion O. Adebiyi, Prisca O. Olawoye and Moses Abiodun
27.1 Introduction
27.2 Related works
27.3 Methods
27.3.1 Principal component analysis
27.3.2 Independent component analysis
27.3.3 Linear discriminant analysis
27.3.4 t-Distributed stochastic neighbor embedding
27.3.5 Nonnegative matrix factorization
27.3.6 Recursive feature elimination
27.3.7 SelectKBest
27.3.8 Minimum redundancy maximum relevance
27.3.9 Least absolute shrinkage and selection operator
27.3.10 Support vector machines
27.3.11 Random forest
27.3.12 K-nearest neighbor
27.3.13 Decision tree
27.3.14 Artificial neural network
27.3.15 Convolutional neural networks
27.4 Discussions
27.5 Conclusion
References
- Edition: 1
- Published: September 25, 2024
- Language: English
SK
Seifedine Kadry
Seifedine Kadry is a Professor in the Department of Mathematics and Computer Science, at Norrof University College, in Norway. He has a Bachelor’s degree in 1999 from Lebanese University, MS degree in 2002 from Reims University (France) and EPFL (Lausanne), PhD in 2007 from Blaise Pascal University (France), HDR degree in 2017 from Rouen University. At present, his research focuses on data Science, education using technology, system prognostics, stochastic systems, and applied mathematics. He is an ABET program evaluator for computing, and ABET program evaluator for Engineering Tech. He is a Fellow of IET, Fellow of IETE, and Fellow of IACSIT. He is a distinguished speaker of IEEE Computer Society.
Affiliations and expertise
Department of Computer Science and Mathematics, Lebanese American University, Beirut, Lebanon; Department of Applied Data Science, Noroff University College, Kristiansand, NorwaySM
Shubham Mahajan
Dr. Shubham Mahajan, a distinguished member of prestigious organizations such as IEEE, ACM, and IAENG, boasts an impressive academic and professional background. He earned his B.Tech. degree from Baba Ghulam Shah Badshah University, his M.Tech. degree from Chandigarh University, and his Ph.D. degree from Shri Mata Vaishno Devi University (SMVDU) in Katra, India.
Dr. Mahajan has a remarkable track record in the field of artificial intelligence and image processing, holding an impressive portfolio of eleven Indian patents, as well as one Australian and one German patent. His contributions to the field are further evidenced by his extensive publication record, which includes over 100+ articles published in peer-reviewed journals, conferences and 10+ books. His research interests span a wide array of topics, encompassing image processing, video compression, image segmentation, fuzzy entropy, nature-inspired computing methods, optimization, data mining, machine learning, robotics, and optical communication. Notably, his dedication and expertise have earned him the 'Best Research Paper Award' from ICRIC 2019, published by Springer in the LNEE series.
In recognition of his exceptional achievements, Dr. Mahajan has received numerous accolades and honours throughout his career. These include the Best Student Award in 2019, the IEEE Region-10 Travel Grant Award in 2019, the 2nd runner-up prize in the IEEE RAS HACKATHON in 2019 (held in Bangladesh), the IEEE Student Early Researcher Conference Fund (SERCF) in 2020, the Emerging Scientist Award in 2021, and the IEEE Signal Processing Society Professional Development Grant in 2021. His commitment to excellence in research was further underscored by his receipt of the Excellence in Research Award in 2023.
Dr. Mahajan's impact extends beyond the realm of academia. He has served as a Campus Ambassador for IEEE, representing esteemed institutions such as IIT Bombay, Kanpur, Varanasi, Delhi, as well as various multinational corporations. His active engagement in fostering international research collaborations reflects his enthusiasm for advancing the frontiers of knowledge and innovation on a global scale.
Affiliations and expertise
Amity School of Engineering & Technology, Amity University Haryana.Read Data Science in the Medical Field on ScienceDirect