Intelligent Prediction of Heart Disease Diagnosis Using ANFIS Classification Model

Cardiovascular diseases are being to become the main cause of death in most countries of the world. The considerable growing of cardiovascular disease and its effects and complications as well as the high costs on society makes medical community seek for solutions to prevention, early identification and effective treatment with lower costs. Thus, valuable knowledge can be established by using artificial intelligence, The discovered knowledge makes improve the quality of service. Heart disease is a term that assigns to a large number of medical conditions related to heart. These medical conditions describe the abnormal health conditions that directly influence the heart and all its parts. Heart disease is a major health problem in todays time. Diagnosis of heart disease by using machine learning methods is one of the challenges in the health field. Technically, the ANFIS performs a vital role for prediction of diseases in medical industry. ANFIS is a fuzzy inference system (FIS) implemented in the framework of an adaptive fuzzy neural network. It combines the explicit knowledge representation of an FIS with the learning power of artificial neural networks. The objective of ANFIS is to integrate the best features of fuzzy systems and neural networks. This paper will be provided on a particular dataset using classification and feature selection approach. In this we will use feature ranking on effective factors of disease related to Cleveland clinic database and by using Novel Feature Selection Method as well as ANFIS, 13 effective factors reduced to 5 optimized features in terms of accuracy. The assessment of selected features of classified methods also showed that NFS method along with ANFIS has the best accurate criteria of the rate of 97.61 % on these features.