Intelligent Heart Disease Prediction Model Using Classification Algorithms

Data mining technique have led over various methods to gain knowledge from vast amount of data. So, different research tools and techniques like association rule, Classification algorithms, and decision tree etc. This paper analyses the performance of various classification function techniques in data mining for prediction heart disease from the heart disease data set. The classification algorithms used and tested in work are Logistics, Multi-layer Perception and Sequential Minimal Optimization algorithms. The performance factor used for analyzing the efficiency of algorithm are clustering accuracy and error rate. The result show logistics classification function efficiency is better than multi-layer perception and sequential minimal optimization.