Machine Learning-Based Cognitive Support System for Healthcare
Body area networks are widely used for monitoring critical illnesses and providing continuous healthcare when the patients and caretakers are not always in proximity. The three components of the closed loop system of healthcare are information of patients collected through sensors, physical tests, and questions; storage and connectivity of the knowledge base with medical experts; and final diagnosis and treatment. The accuracy of a system is challenged by the accuracy of the sensors used; diagnosis with insufficient information; compatibility, feasibility, and availability of technology; storage, speed, power requirements; and security. Designing a cognitive support system using machine learning algorithms to do an initial analysis and narrow down the possibilities of ailments will help physicians to accurately diagnose and recommend appropriate treatment. The support system has to be trained with historical data beforehand and the same made use of in connecting with medical experts.
KeywordsWireless body area network Machine learning Medical data Ethics Cognitive support system for healthcare Machine learning Invasive and non-invasive sensors
The authors thank the authorities of Kumaraguru College of Technology, Coimbatore for providing excellent computing facilities and encouragement.
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