HEART DISEASE DECISION SUPPORT SYSTEM USING DATA MINING CLASSIFICATION MODELING TECHNIQUES

Abstract

The healthcare industry collects huge amounts of healthcare data which, unfortunately, are not “mined” to discover hidden information for effective decision making by healthcare practitioners. Discovery of hidden patterns and relationships often goes unexploited. Advanced data mining modeling techniques can help remedy this situation. This research has developed a prototype Heart Disease Decision Support System (HDDSS) using Data Mining Classification Modeling Techniques, namely, Decision Trees, Naïve Bayes and Neural Network. Results show that each technique has its own strength in realizing the objectives of the defined mining goals. HDDSS 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 heart disease. It enables significant knowledge, e.g., patterns, relationships between medical factors related to heart disease, to be established. HDDSS is Web-based, user-friendly, scalable, reliable and expandable. It is implemented on the .NET platform.