This unique book introduces a variety of techniques designed to represent, enhance and empower multi-disciplinary and multi-institutional machine learning research in healthcare informatics.

Chapter 1: Machine learning for Healthcare: Introduction.- Chapter 2: Artificial Intelligence in Medical Diagnosis: Methods, algorithms and applications.- Chapter 3: Intelligent Learning Analytics in Healthcare Sector Using Machine Learning.- Chapter 4: Unsupervised Learning on Healthcare Survey Data with Particle Swarm Optimization.- Chapter 5: Machine Learning for Healthcare Diagnostics.- Chapter 6: Disease Detection System (DDS) Using Machine Learning Technique.- Chapter 7: Knowledge Discovery (Feature Identification) from Teeth, Wrist and Femur Images to determine Human Age and Gender.- Chapter 8: Deep Learning Solutions for Skin Cancer Detection and Diagnosis.- Chapter 9: Security of Healthcare Systems with Smart Health Records using Cloud Technology.- Chapter 10: Intelligent Heart Disease Prediction on Physical and Mental Parameters: A ML Based IoT and Big Data Application and Analysis.- Chapter 11: Medical Text and image processing: Applications, issues and challenges.- Chapter12: Machine Learning Methods for Managing Parkinson’s Disease.- Chapter 13: An Efficient Method for Computer-aided Diagnosis of Cardiac Arrhythmias.- Chapter 14: Clinical decision support systems and predictive analytics.- Chapter 15: Yajna and Mantra Science Bringing Health and Comfort to Indo-Asian Public: A Healthcare 4.0 Approach and Computational Study.- Chapter 16: Identifying Diseases and Diagnosis using Machine Learning.

This unique book introduces a variety of techniques designed to represent, enhance and empower multi-disciplinary and multi-institutional machine learning research in healthcare informatics. Providing a unique compendium of current and emerging machine learning paradigms for healthcare informatics, it reflects the diversity, complexity, and the depth and breadth of this multi-disciplinary area. Further, it describes techniques for applying machine learning within organizations and explains how to evaluate the efficacy, suitability, and efficiency of such applications. Featuring illustrative case studies, including how chronic disease is being redefined through patient-led data learning, the book offers a guided tour of machine learning algorithms, architecture design, and applications of learning in healthcare challenges.

Provides a unique compendium of current and emerging machine learning paradigms for healthcare informatics Reflects the diversity, complexity, and the depth and breadth of this multi-disciplinary area Offers a guided tour of machine learning algorithms, architecture design, and applications of learning in healthcare challenges Presents a variety of techniques designed to represent, enhance, and empower multi-disciplinary and multi-institutional machine learning research in healthcare informatics

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