Machine Learning and IoT Applications for Health Informatics

Recently medical informatics, especially health informatics, has received various applications from machine learning and IoT. The applications of machine learning and IoT technology have entirely changed the predictive capability of the concerned disease. The input data to the machine learning and IoT-based devices are sometimes not structured; they could be unstructured as well; therefore, analyzing such unstructured data has significance. These data could be image related such as X-Ray images, ECG images, and others. Therefore, this edited book will focus on structure and unstructured data applications. Sickness and health-related data collection are also significant benefits of health analytics. Finally, further progress in the patients' health is made, and decisions are taken on further treatments based on the data. The Internet of Things (IoT) has emerged as a preferred solution to many emerging problems in the last few years. This colligated ecosystem in electronic devices can be worn as accessories and embedded in clothing. Also, the IoT-related apps have helped the data collection process and contributed to information technology. The interesting fact is that IoT applications can be found more in the healthcare system, especially healthcare informatics. IoT-powered applications in healthcare immensely benefit patients and physicians, hospitals, and overall healthcare systems. The wearables devices that are enabled with machine learning and IoT are changing the form of wearables like fitness bands, measuring blood pressure, and checking heart rate monitoring and glucometer concepts. IoT and machine learning-enabled health care systems can change the treatment's efficiency and quality on the treatment front. It can monitor in real-time about the conditions of the patients, and with the use of app-based smartphones, the dynamics of the treatments are changing forever. Therefore, the delivery model of the integrated services of health care using IoT and machine learning will completely change the treatment of heart diseases, kidney disease, hypertension, and other diseases. The care model for patients will be completely different. This edited book will address the problems mentioned above and shall provide solutions. Each chapter shall address a unique machine and IoT-enabled application for health-related problems. The key features of this edited book are: 1. Application related to the amalgamations of machine learning and IoT for medical data 2. Explores the disease diagnosis incorporation powered by IoT and enabled with predictive models 3. Recent advancements in machine learning and deep learning models in health analytics. 4. Digs into cost reduction, treatment improvement, quick disease diagnosis, and drug and equipment management of healthcare using IoT systems. 5. Presents several case studies related to machine learning, deep learning, and IoT applications toward health analytics.