Problem Formulation of Heart Attack Analysis

Problem Formulation of Heart Attack Analysis

Author Name : Dr. T. A. Albinaa, K. Sudarshana

ABSTRACT

The health care industries collect huge amounts of data that contain some hidden information, which is useful for making effective decisions. For providing appropriate results and making effective decisions on data. Some advanced data mining techniques are used. In this study, a Heart Attack Possibility Prediction is developed using Naïve Bayes and Decision Tree algorithms for predicting the risk level of heart disease. The system uses 13 parameters such as age, sex, blood pressure, cholesterol and obesity for prediction. This system predicts the likelihood of patients getting heart disease. It enables significant knowledge. Eg: relationships between medical factors related to heart disease and patterns, to be established. The process have employed the multilayer perception neural network with backpropagation as training algorithm.  

Key Words: data mining techniques, heart attack possibility prediction, naïve bayes and decision tree algorithm, multilayer perception, backpropagation.