A likelihood swarm whale optimization based LeNet classifier approach for the prediction and diagnosis of patients with atherosclerosis disease

Coronary Artery Disease (CAD) caused by atherosclerosis is having huge impact and is considered an epidemic one in all over the world. Cardio vascular disease (CVD) in 2019 records is about 32% of global death rate. Among these deaths, 85% were caused by heart attack and stroke. Atherosclerosis is regarded as a condition at which the arteries become hardened and narrowed due to the plaque accumulation around the walls of arteries. The disease growth is slow, asymptomatic, sudden cardiac arrest, myocardial infarction and stroke. At present, medical diagnostic techniques are widely applied for the prediction of disease. However, they are uncommon in the desired sensitivity and resolution for detection. The lack of non-invasive diagnosing tool for the prediction of disease in early stage limits the treatment and prevention of patients having various degrees. This proposed research work focuses on intelligent optimization technique named Maximum Likelihood Swarm Whale Optimization (MLSWO) that is used to extract the crucial features in the Atherosclerosis (STULONG) and Kaggle datasets and predict the disease progression. The outcomes the selected features are classified using LeNet classifier for assorting the individuals. The proposed MLSWO algorithm produces higher accuracy rate of 99.2%, sensitivity rate of 98.36% and specificity of 100% compared with other state-of art techniques.