Heart Disease Prediction and Treatment Suggestion Using Machine Learning

Abstract

Disease anticipation systems are the better alternatives, to avoid the human errors in disease diagnosis and also assist in disease interference. Nowadays, the number of heart disease patients is increasing so we need an optimal heart disease prediction and treatment suggestion system. Heart disease dataset preparation, prediction system’s process flow design, process execution and results evaluation are the most common life cycle modules of any heart disease prediction system. Input dataset attributes modeling, attribute risk factor calculation; threshold determination and achieving the high accuracy in disease prediction are the major limitations of the existing heart disease prediction and treatment proposal systems. Keywords: Machine learning, Decision tree, Logistic regression.