An Intelligent and Electronic System based Classification and Prediction for Heart Disease Diagnosis

The healthcare industry collects massive 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 An Intelligent System based Classification and Prediction for Heart Disease
Diagnosis 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. An
Intelligent System based Classification and Prediction for Heart Disease Diagnosis using data mining
techniques can answer complex “what if” queries which traditional decision support systems cannot. Using
medical profiles such as age, sex, L.V and Ejection Fraction 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.
Index Termsـــــــ Data Mining, Ejection Fraction, Heart Disease, Decision Support System, Classification
Techniques, intelligent system