Comprehensive Analysis of Atherosclerosis Disease Prediction using Machine Learning

Abstract

The prediction of atherosclerosis disease is a very complex process. The complexity of prediction faces a problem of multiple attributes of heart disease-related symptoms. The machine learning algorithm plays an essential role in the prediction of atherosclerosis disease. The various authors and research scholar proposed various algorithms based on machine learning and artificial intelligence. This paper presents the study of machine learning algorithms for the prediction of atherosclerosis disease. The analysis of atherosclerosis disease applied four machine learning algorithms such as support vector machine, KNN, decision tree and naïve Bayes. For the validation of algorithms applied four datasets: the applied dataset obtained from the UCI machine learning repository. For the evaluation of performance, measure three significant parameters like accuracy, specificity and sensitivity. The all-experimental work done in MATLAB environments. MATLAB is algorithm analysis software. The analysis of performance suggests that support vector machine is better than other machine learning algorithms.