Healthcare analytics by engaging machine learning

Abstract

Precise prediction of chronic diseases is the very basis of all healthcare informatics. Early diagnosis of the disease is crucial in delivering any healthcare service. The modern times witness our general vulnerability to several health disorders due to a stressful lifestyle causing anxiety and depression, or susceptibility to hypertension and diabetics or major diseases such as cancer or cardiovascular ailments. Hence, we should undergo periodic screening and diagnostic tests for such possible disorders to lead healthy lives. In this context, Machine Learning technology can play a pivotal role in developing Electronic Health Records (EHR) for implementing quick and comprehensively automated procedures in disease detection among the at-risk individuals at an early stage, so that accelerated processes of referral, counseling, and treatment can be initiated. The scope of the current paper is to survey the utilization of feature selection and techniques of Machine Learning, such as Classification and Clustering in the specific context of disease diagnosis and early prediction. This paper purposes of identifying the best models of Machine Learning duly supported by their performance indices, utility aspects, constraints, and critical issues in the specific context of their effective application in healthcare analytics for the benefit of practitioners and researchers.