Cardiac disease prediction using naïve bayes machine learning algorithm

Abstract

With the increasing population and comforts many of the people with white collar jobs will not have enough time or don’t like to spend time on doing exercises or doing physically stress causing works which leads to a lot of serious problems that include blood pressure, heart diseases and etc. If any of these problems are left unattended or not recognized prior, these may become deadly. For example, some heart diseases like heart stroke or heart attacks can be easily prevented if the patients are treated with better medication. From this idea of predicting the heart diseases prior gave a spark to this research area of using machine learning techniques to predict the possibility of a person that may get heart diseases soon or later based on some simple answers to the questions that are given as the attributes to the naive Bayes classification that will in turn provide the best results possible. Naive Bayes is not only an easy algorithm but also a powerful one which can easily handle big data sets. With the help of this system, there can be a good an outbreak of the people getting self-conscious about the current condition they are in and get health conscious, which can pretty well help them in getting themselves out of the health troubles. Prediction and classification all add up to data mining, but the giant data sets and the accuracy for it can only be done right by Naive Bayes, which is a well known algorithm which is being used from many decades and still works better than the algorithms that are invented after it.