A COMPARATIVE ANALYASIS OF MACHINE LEARNING TECHNIQUES FOR HEART DISEASE PREDICTION

Abstract

Heart disease, alternatively known as cardiovascular disease, encases various conditions that impact
the heart and is the primary basis of death worldwide over the span of the past few decades. It associates many
risk factors in heart disease and a need of the time to get accurate, reliable, and sensible approaches to make an
early diagnosis to achieve prompt management of the disease. Data mining is a commonly used technique for
processing enormous data in the healthcare domain. Researchers apply several data mining and machine
learning techniques to analyse huge complex medical data, helping healthcare professionals to predict heart
disease. This research paper presents various attributes related to heart disease, and the model on basis of
supervised learning algorithms as Naïve Bayes, decision tree, K- nearest neighbor, and random forest algorithm.
It uses the existing
dataset from the Cleveland database of UCI repository of heart disease patients. The dataset comprises 303
instances and 76 attributes. Of these 76 attributes, only 14 attributes are considered for testing, important to
substantiate the performance of different algorithms. This research paper aims to envision the probability of
developing heart disease in the patients. The results portray that the highest accuracy score is achieved with K
nearest neighbor.