Supervised Learning-Based Classifiers in Healthcare Decision-Making

Abstract

Supervised classifiers are machine learning classifiers that can predict the present categorical class label if similar information from the past is given. The simple and easy nature of such classifier makes it popular in different applications. Healthcare is one such application area which has recently adopted this computerized decision-making approach to assist the experts and speed up the process. Also advances in healthcare electronics is generating a lot of data and making it easily available. The reference for designing a reliable decision-making system can be obtained from these easily available datasets. This paper briefs the recently adopted machine learning techniques in healthcare with special focus on the supervised classifier algorithms, its application to healthcare decision-making and their evaluation. These classifiers are applied to a Parkinson’s disease benchmark dataset for validation.