Issues and Challenges associated with Machine Learning Tools for Health Care System : A Review

Abstract

Support of Artificial intelligence can be used to update the traditional healthcare services and it can serve the society in efficient manner. Using machine learning tools, diagnosis process can be automated and large scale clinical data can be processed by practitioners to generate quick medical advisory for patients. Main objective of this chapter is to highlight the contribution of machine learning tools in the medical domain for the period (2017-2020). It also discusses about prediction schemes for patient readmission cycle, hypertension, heart diseases/cancer symptoms, health status of elderly patient’s mental disorders, anomaly status, patient’s dissatisfaction after surgical operations, children health care & malnutrition level etc. for healthcare industry. Study shows that drug discovery and human trials using machine learning algorithms can be used to identify the disease behavior over the patients as well as reaction of drugs over the patient health. Automated drug trials can reduce the overall drug development cost as well as drug's trials can be performed without using live entities (human/animal). Surgical operations can be automated with machine learning assistance and its side effects can be predicted to minimize the risk associated with patient’s health. A machine learning framework can be developed to improve the healthcare services using this review study.