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.
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.
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.
Autor*in
Department of Computer Science and Mathematics, Lebanese American University, Beirut, Lebanon; Department of Applied Data Science, Noroff University College, Kristiansand, Norway
Amity School of Engineering & Technology, Amity University Haryana.
1. PPH 4.0: a privacy-preserving health 4.0 framework with machine learning and cellular automata
2. An automatic detection and severity levels of COVID-19 using convolutional neural network models
3. Biosensors and disease diagnostics in medical field
4. Brain tumor recognition and classification techniques
5. Identifying the features and attributes of various artificial intelligence-based healthcare models
6. Classification algorithms and optimization techniques in healthcare systems representation of dataset in medical applications
7. A knowledge discovery framework for COVID-19 disease from PubMed abstract using association rule hypergraph
8. Predictive analysis in healthcare using data science: leveraging big data for improved patient care
9. Data science in medical field: advantages, challenges, and opportunities
10. Decentralizing healthcare through parallel blockchain architecture: transmitting internet of medical things data through smart contracts in telecare medical information systems
11. Machine learning in heart disease prediction
12. U-Net-based approaches for brain tumor segmentation
13. Explainable image recognition models for aiding radiologists in clinical decision making
14. Prediction of heart failure disease using classification algorithms along with performance parameters
15. Cancer survival prediction using artificial intelligence: current status and future prospects
16. Heart disease prediction in pregnant women with diabetes using machine learning
17. Healthcare using image recognition technology
18. Integration of deep learning and blockchain technology for a smart healthcare record management system
19. Internet of things based smart health and attendance monitoring system in an institution for COVID-19
20. Medical diagnosis using image processing techniques
21. Harnessing the potential of predictive analytics and machine learning in healthcare: empowering clinical research and patient care
22. Predictive analysis in healthcare using data science
23. Recommender systems in healthcare-an emerging technology
24. Robotics: challenges and opportunities in healthcare
25. A new era of the healthcare industry using Internet of Medical Things
26. Single cell genomics unleashed: exploring the landscape of endometriosis with machine learning, gene expression profiling, and therapeutic target discovery
27. Analyzing the success of the thriving machine prediction model for Parkinson's disease prognosis: a comprehensive review
