ABSTRACT
Heart disease is the most effective cause of death in the world. Machine learning has been using modern techniques to diagnose the disease at early stages to help doctors give precise treatment to save lives. Many techniques exist to diagnose heart disease. The crucial task is to recognize heart disease early and accurately, so that we can decrease the death rate due to heart disease. By 2030, twenty-five million people will die from heart disease. Though much research is done and many methods have been proposed for diagnosing heart disease from the extensive amount of heart disease data, there are currently no effective techniques, or those techniques are not properly used. In this chapter we used different algorithms, K Nearest Neighbor (KNN), Support Vector machine (SVM), Random Forest (RF) classifier, and Naive Bayes, with the goal of performing predictive analysis using such data mining algorithms and Machine Learning algorithms to analyze the techniques that are most effective.
