Heart Disease Prediction System using Naive Bayes and Jelinek-mercer smoothing

Abstract: Data Mining is non trivial extraction of implicit data, previously not known, and imaginably useful information from data. Data mining is an essential process where intelligent methods are applied in order to extract data patterns. Using data mining we can evaluate patterns which we can use in future to take intelligent decisions and we can present the knowledge we extracted in better way. Data Mining refers to using a variety of techniques to identify information or decision making knowledge in the database and extracting these in a way that they can put to use in areas such as decision making, predictions, for valuable forecasting and computation. The healthcare industry collects huge amounts of healthcare data which, unfortunately, are not “mined” to discover hidden information, to take decisions effectively, to discover the relations that connect parameters in a database is the subject of data mining. This research work has developed a Decision Support in Heart Disease Prediction System (HDPS) using data mining modelling technique, namely, Naïve Bayes. Using medical profiles such as age, sex, blood pressure and blood sugar, chest pain, ECG graph etc it can predict the likelihood of patients getting a heart disease. It is implemented in matlab as an application which takes medical test’s parameter as an input. It can be used as a training tool to train nurses and medical students to diagnose patients with heart disease.

Keywords: Data mining, Jelinek-mercer smoothing for Naive Bayes, heart disease, Naïve Bayes, decision support