Machine Learning in Biomedical and Health Informatics

With rapid development in healthcare, the domain of health analytics via IDA (intelligent data analysis) and health informatics is continuously growing. Machine learning is playing an indispensable role in framing clinical decisions and enhancing its accuracy. This new book, Machine Learning in Biomedical and Health Informatics: Current Applications and Challenges, is a comprehensive take on the field of biomedical and health informatics discussing topics that include predictive health analytics, pandemic management, AI ethics, application and integration of Internet of Things and Machine Learning for effective healthcare, and more. The book covers a range of bioinformatics tools and methods and their relation to drug designing and drug screening using ML. Several chapters cover clustering techniques and other methods for analyzing human heart-related disorders. The authors also explore the use of ML in creating adaptive therapies, for example, for using chemotherapy and androgen deprivation therapy for prostate cancer. Use of ML for tracking diseases such as Parkinson's speech, Covid-19, and others and survival rates of deadly diseases like cancer are also discussed. The book also demonstrates a framework for big data classification using singular value decomposition, which is applied to various medical datasets. Also discussed is medical images analysis and using different AI techniques for solving problem areas in medical images by considering X-rays, MRI, PET. Various case studies are also included that demonstrate the practical use of ML in healthcare informatics. The book also reviews a few major applications of ML in bioinformatics like identification of patterns and relationships in genomic data. This book will prove beneficial for researchers and technocrats as well as for students, providing an in-depth and illustrated work on the use of machine learning in biomedical and health informatics.