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

Abstract

The healthcare industry collects massive amounts of healthcare data which, unfortunately, are not “mined” to discover hidden 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, Naive 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. INTRODUCTION Data Mining is a non-trivial extraction of implicit, previously unknown and potential useful about data [1][13] . In short, it is a process of analyzing data from different Perspective and gathering the knowledge from it, the discovered knowledge can be used for different applications for example healthcare industry. The health care industry is generally information rich, which is not feasible to handle manually. These large amounts of data are very important in the field of data mining to extract useful and generate relationships amongst the attributes. Heart disease Diagnosis is a complex task which requires much Experience and knowledge. In the health care industry the data mining is mainly used for predicting the diseases from the datasets. The data mining techniques, namely decision trees, Naive bayes, and neural networks are analyzed on heart disease database [2] .Medical practitioners generate data with a wealth of hidden present. For classification and prediction, unused data must be converted into a dataset for modeling using different data mining methods. In the proposed work, an intelligent system based classification and