Абстрактный

Comparative Analysis of Classification Function Techniques for Heart Disease Prediction

Dr. S.Vijayarani, S.Sudha

The data mining can be referred as discovery of relationships in large databases automatically and in some cases it is used for predicting relationships based on the results discovered. Data mining plays an important role in various applications such as business organizations, e-commerce, health care industry, scientific and engineering. In the health care industry, the data mining is mainly used for Disease Prediction. Various data mining techniques are available for predicting diseases namely clustering, classification, association rules, regression and etc. This paper analyses the performance of various classification function techniques in data mining for predicting the heart disease from the heart disease data set. The classification function algorithms used and tested in this work are Logistics, Multi Layer Perception and Sequential Minimal Optimization algorithms. Comparative analysis is done by using Waikato Environment for Knowledge Analysis or in short, WEKA. It is open source software which consists of a collection of machine learning algorithms for data mining tasks. The performance factors used for analysing the efficiency of algorithms are clustering accuracy and error rate. The result shows that logistics classification function efficiency is better than multi layer perception and sequential minimal optimization.