| Description |
1 online resource (251 p.) |
| Series |
River Publishers Series in Computing and Information Science and Technology Series |
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River Publishers Series in Computing and Information Science and Technology
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| Contents |
Cover -- Half Title -- Series Page -- Title Page -- Copyright Page -- Table of Contents -- Preface -- List of Contributors -- List of Figures -- List of Tables -- List of Abbreviations -- Chapter 1: Investigation on Improving the Performance of Class-imbalanced Medical Health Datasets -- 1.1: Introduction -- 1.2: Problem Formulation -- 1.3: Techniques to Handle Imbalanced Datasets -- 1.3.1: Random undersampling (RUS) -- 1.3.2: Random oversampling (RUS) -- 1.3.3: Synthetic minority oversampling technique (SMOTE) -- 1.4: Classification Models -- 1.4.1: Naive Bayes |
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1.4.2: k-Nearest neighbor classifier -- 1.4.3: Decision tree classifier -- 1.4.4: Random forest -- 1.5: Dataset Collection -- 1.5.1: Heart failure clinical records dataset -- 1.5.2: Diabetes dataset -- 1.6: Experimental Results and Discussion -- 1.6.1: Heart failure clinical records dataset -- 1.6.2: Diabetes dataset -- 1.7: Conclusion -- References -- Chapter 2: Improving Heart Disease Diagnosis using Modified Dynamic Adaptive PSO (MDAPSO) -- 2.1: Introduction -- 2.2: Background and Related Work -- 2.2.1: PSO modification -- 2.2.2: Feature selection -- 2.2.3: Classification and clustering |
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2.3: Proposed Approach -- 2.3.1: PSO algorithm -- 2.3.2: Inertia weight -- 2.3.3: Modified dynamic adaptive particle swarm optimization (MDAPSO) -- 2.4: Experimental Setup and Results -- 2.4.1: Dataset -- 2.4.2: Performance metrics -- 2.4.3: Result -- 2.5: Conclusion -- References -- Chapter 3: Efficient Diagnosis and ICU Patient Monitoring Model -- 3.1: Introduction -- 3.2: Main Text -- 3.2.1: Disease prediction -- 3.2.2: Hospital monitoring system -- 3.3: Experimentation -- 3.3.1: The threshold for the Levenshtein distance -- 3.3.2: The threshold for heart rate and respiratory rate |
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3.4: Conclusion -- References -- Chapter 4: Application of Machine Learning in Chest X-ray Images -- 4.1: Introduction -- 4.2: Chest X-ray Images -- 4.3: Literature Review -- 4.4: Application of Machine Learning in Chest X-ray Images -- 4.4.1: Clustering -- 4.4.2: Regression -- 4.4.3: Segmentation -- 4.4.4: Classification -- 4.5: Case Study: Lung Chest X-ray Images -- 4.5.1: Methodology -- 4.5.2: JSRT dataset -- 4.5.3: Image pre-processing -- 4.5.4: CNN -- 4.6: Conclusion -- 4.7: Future Study -- References -- Chapter 5: Integrated Solution for Chest X-ray Image Classification -- 5.1: Introduction |
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5.2: Related Work -- 5.3: The Method -- 5.3.1: Feature extraction -- 5.3.2: Feature reduction -- 5.3.3: Classification -- 5.3.4: Algorithm -- 5.4: Experimental Results -- 5.5: Discussion and Conclusions -- References -- Chapter 6: Predicting Genetic Mutations Among Cancer Patients by Incorporating LSTM with Word Embedding Techniques -- 6.1: Introduction -- 6.2: Related Work -- 6.2.1: Basic feature engineering -- 6.2.2: Classification method -- 6.3: Data Overview -- 6.3.1: Dataset structure -- 6.3.2: Data pre-processing -- 6.3.3: Most frequent genes and class -- 6.3.4: Most frequent variation and class |
| Summary |
This book provides various insights into machine learning techniques in healthcare system data and its analysis |
| Notes |
Description based upon print version of record |
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C. Karthik is an Associate Professor of Mechatronics Engineering at Jyothi EngineeringCollege where he teaches courses on robotics and automation, mechatronics system design, and optimization algorithms. He has 12 years university-level teaching experience in electrical and computer engineering and has a strong CV about research activities in control system design and artificial intelligence. He holds a large number of patents and has received several medals and awards due to his innovative work and research activities. He is guest editor and editorial board member for many journals and has published several international journals papers. Karthik has also made several conference presentations and worked on the development and evaluation of several interactive computing projects. His research interests include the time delay control problem, nonlinear system identification, robotics and autonomous systems, sensor design and unmanned vehicles. He was recently involved in the research of sensor design for medical autonomous systems using machine learning techniques. Karthik is a member of the Association for Computing Machinery (ACM), the ACM Special Interest Group on Computer Human Interaction (SIGCHI), a senior member of IEEE, and a member of the IEEE Robotics and Automation Society.M. Rajalakshmi received her Ph.D. degree from Anna University, in 2020, in the area of system identification and controller tuning. She received her B.Eng. degree in electronics and instrumentation engineering from the Kamaraj College of Engineering and Technology, in 2010, and M.Tech. degree in instrumentation and control engineering from the Kalasalingam Academy of Research and Education (KARE), in 2012. She is working as an Associate Professor with Jyothi Engineering College, Thrissur, Kerala. She has published several international journal and conference papers. Her professional interests focus on machine learning, artificial intelligence, linear and nonlinear control systems, system identification, and her current projects include modeling and controlling of nonlinear process (machine learning algorithms for biomedical and robotics).Dr. Sachi Nandan Mohanty is an Associate Professor in the Department of Computer Engineering at the College of Engineering Pune, India. He received his post doctorate from IIT Kanpur in the year 2019 and Ph.D. from IIT Kharagpur, India in the year 2015, with a HRD scholarship from the Government of India. He has edited 14 books in association with Springer, Wiley and CRC Press. His research areas include data mining, big data analysis, cognitive science, fuzzy decision making, the brain-computer interface, and computational intelligence. Professor Mohanty received three best paper awards during his Ph.D. at IIT Kharagpur, from an International Conference at Beijing, China, and the other at International Conference on Soft Computing applications organized by IIT Rookee in the year 2013. He was awarded a best thesis award by the Computer Society of India in the year 2015. He has published 42 papers in international journals of repute and has been elected as a fellow of the Institute of Engineers, IETE, and a senior member of the IEEE Computer Society Hyderabad chapter. He is also a reviewer for Journal of Robotics and Autonomous Systems (Elsevier), Computational and Structural Biotechnology Journal (Elsevier), Artificial Intelligence Review (Springer), and Spatial Information Research (Springer).Mr. Subrata Chowdhury is perusing M.Tech at the Sreenivasa Institute of Technology and Management Studies, Chittoor Andra Pradesh, India. He has edited 5 books in association of the CRC press and others. His research areas include data mining, big data, Machine learning, Quantum Computing, Fuzzy logic, AI, Edge Computing, Swarm Intelligence, Healthcare. He receive Awards and Nominations from the different National & International Science societies. He had published more then 50 papers in international and reputed journals. He has been the member of the IET and a member of the IEEE. He is also the reviewers for the IEEE Transactions, Elsevier's, Springers. And Academic editor for the Hindwai journals. He has been invited as a keynote speakers for many Workshops, Conferences and Seminars |
| Subject |
Machine learning.
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Artificial intelligence -- Medical applications.
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Medical informatics.
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Machine Learning
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COMPUTERS / Information Technology
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Artificial intelligence -- Medical applications.
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Machine learning.
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Medical informatics.
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| Form |
Electronic book
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| Author |
Chandran, C. Karthik
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Rajalakshmi, M.
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Mohanty, Sachi Nandan.
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Chowdhury, Subrata.
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| ISBN |
9781000959994 |
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1000959996 |
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8770228116 |
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9788770228114 |
|
9781000959987 |
|
1000959988 |
|
9788770228107 |
|
8770228108 |
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9781003438816 |
|
1003438814 |
|
