Intelligent heart disease prediction system using data mining techniques

Authors:
Sellappan Palaniappan;Rafiah Awang
Affiliations:
Department of Information Technology, Malaysia University of Science and Technology, Block C, Kelana Square, Jalan SS7/26 Kelana Jaya, 47301 Petaling Jaya, Selangor, Malaysia;Department of Information Technology, Malaysia University of Science and Technology, Block C, Kelana Square, Jalan SS7/26 Kelana Jaya, 47301 Petaling Jaya, Selangor, Malaysia
Venue:
AICCSA '08 Proceedings of the 2008 IEEE/ACS International Conference on Computer Systems and Applications
Year:
2008

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Abstract

The healthcare industry collects huge amounts of healthcare data which, unfortunately, are not “mined” to discover hidden information for effective decision making. Discovery of hidden patterns and relationships often goes unexploited. Advanced data mining techniques can help remedy this situation. This research has developed a prototype Intelligent Heart Disease Prediction System (IHDPS) using data mining techniques, namely, Decision Trees, Naïve Bayes and Neural Network. Results show that each technique has its unique strength in realizing the objectives of the defined mining goals. IHDPS can answer complex “what if” queries which traditional decision support systems cannot. Using medical profiles such as age, sex, blood pressure and blood sugar it can predict the likelihood of patients getting a heart disease. It enables significant knowledge, e.g. patterns, relationships between medical factors related to heart disease, to be established. IHDPS is Web-based, user-friendly, scalable, reliable and expandable. It is implemented on the .NET platform.