• G. Sunitha
• Dr.N. Geethanjali

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 and data mining; the discovered knowledge makes improve the quality of service. Until now, different researches have been carried out in order to predict heart disease based on data mining methods such as classification and feature selection methods; however, what has been less noticed is the exact diagnosis of disease with the lowest cost and time. Early detection and treatment of heart disease will reduce the patient mortality rate. Accordingly, herein propose a Particle Swarm Optimization (PSO) and highly accurate hybrid Fuzzy Extreme Learning Machine (FELM) method for the diagnosis of coronary artery disease. The proposed FELM based prediction model is able to detect coronary artery disease based on clinical data without the need for invasive diagnostic methods. Making use of such methodology, we achieved good accuracy, sensitivity and specificity rates on Z-Alizadeh Sani dataset.

Diagnosis Systems, Heart Disease, Feature Selection, Fuzzy Extreme Learning, Machine Learning, Coronary Artery Disease.

"FEATURE SELECTION AND FUZZY EXTREME LEARNING MACHINE (FELM) CLASSIFIER FOR HEART DISEASE DIAGNOSIS ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.5, Issue 8, page no.809-821, August-2018, Available :http://www.jetir.org/papers/JETIRC006456.pdf

"FEATURE SELECTION AND FUZZY EXTREME LEARNING MACHINE (FELM) CLASSIFIER FOR HEART DISEASE DIAGNOSIS ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.5, Issue 8, page no. pp809-821, August-2018, Available at : http://www.jetir.org/papers/JETIRC006456.pdf