DEVELOPED A HYBRID IMPROVED WEIGHED CUCKOO SEARCH WITH A DEEP MASK CONVOLUTIONAL NEURAL NETWORK TO PREDICT THE HEART DISEASE AT AN EARLY STAGE

Abstract

The biggest medical issue or obstacle that contemporary medicine faces worldwide is cardiovascular disease. It is now a major contributing element to the rising death rate. If heart illness is not detected early on, its severity is much greater and may have dangerous repercussions. Techniques include electronic records of health, ongoing body surveillance via a computer system, consumer health status diagnosis using wearable devices, and healthcare device projections on the bodies of humans.A heart attack diagnosis is discovered to be a major problem, so to take the necessary steps, a diagnosis must be made remotely and frequently. Finding the incidence of coronary artery disease has emerged as a major field of inquiry for scientists in contemporary society and multiple approaches have recently been developed. An important factor in the very accurate detection of cardiac issues is the optimization method. A hybrid Improved Weighed Cuckoo Search with a Deep Mask Convolutional Neural Network (IWCS-DMCNN) approach has been presented to identify heart disease at an early stage.The proposed method is more efficient than other existing methods with a 98.59%. In comparison to an existing methods, the evaluation of the proposed approach improves the performance measures.