Medical Diagnosis of Cardiac Disease using Naïve Bayes Algorithm

In recent days the health industry has collected vast amounts of patient data, which unfortunately is not produced in order to give some hidden information, and thus to make effective decisions, which are connected with the base of the patient's data and are subjected to data mining. This work has developed a Decision Support in Heart Disease Prediction System (HDPS) using data mining modelling techniques namely Naïve Bayes and K-means, which are one of the most popular techniques. Using of medical data such as age, sex, blood pressure, blood sugar levels, chest pain etc. we can predict the likelihood of the patient. Most hospitals today use decision-support systems, but to get the results of the disease are largely limited i.e., Determine based on gender, marital status and who have been treated for heart failure. Solutions are always made in a hospital based on intuition and experience of doctors, and not on the rich knowledge data that are hidden in the database. This process leads to undesirable biases, errors and unnecessary health care costs, which affects the quality of services provided to patients.This will help make the diagnostic process more objective and more reliable.