• Naheeda Taranum
• Nirmala hiremani

Cardiovascular disease is one of the most rampant causes of death around the world and was deemed as a major illness in Middle and Old ages. Coronary artery disease, in particular, is a widespread cardiovascular malady entailing high mortality rates. Angiography is, more often than not, regarded as the best method for the diagnosis of coronary artery disease; on the other hand, it is associated with high costs and major side effects. Much research has, therefore, been conducted using machine learning and data mining so as to seek alternative modalities. Accordingly, we herein propose a highly accurate hybrid method for the diagnosis of coronary artery disease. As a matter of fact, the proposed method is able to increase the performance of neural network by approximately 10% through enhancing its initial weights using genetic algorithm which suggests better weights for neural network. Making use of such methodology, we achieved accuracy, sensitivity and speci?city rates of 93.85%, 97% and 92% respectively, on Z-Alizadeh Sani Dataset

"Cardiovascular disease, coronary artery disease, Neural network , Genetic algorithm.", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 6, page no.673-682, June 2019, Available :http://www.jetir.org/papers/JETIR1906N02.pdf

"Cardiovascular disease, coronary artery disease, Neural network , Genetic algorithm.", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 6, page no. pp673-682, June 2019, Available at : http://www.jetir.org/papers/JETIR1906N02.pdf