Classification of Healthcare Datasets through Supervised Machine Learning Algorithms

Abstract

The work centered on methodologies based on machine learning to develop applications that are capable of
recognizing and disseminating health information. In this paper, a different type of supervised machine learning approach is
used for the classification. Analyzing the machine learning algorithms and finding out the most appropriate algorithms for
healthcare data. In this study, designed a classification system using a Decision tree, Naïve Bayes Support Vector Machine,
and KNN for medical data classification with various numbers of attributes and instances. Its include two type classification
namely present or absence data distribution from the Cleveland heart disease data set. The experiment outcomes positively
demonstrate that the decision tree classifier is effective in undertaking healthcare data classification tasks.