Key result
The proposed Maximum Likelihood Swarm Whale Optimization (MLSWO) algorithm with LeNet classifier achieved an accuracy rate of 99.2%, sensitivity of 98.36%, and specificity of 100% for predicting atherosclerosis.
Why the study?
Existing medical diagnostic techniques lack desired sensitivity and resolution, and the lack of a non-invasive diagnostic tool for early-stage atherosclerosis limits treatment and prevention.
Does the MLSWO algorithm with LeNet classifier improve the prediction and diagnosis of atherosclerosis compared to other state-of-the-art techniques?
Does the MLSWO algorithm with LeNet classifier improve the prediction and diagnosis of atherosclerosis compared to other state-of-the-art techniques?
A novel machine learning approach using MLSWO and LeNet classifier demonstrated high accuracy (99.2%) in predicting atherosclerosis from public datasets.
May aid early atherosclerosis detection in practice; extends prior ML methods with superior metrics.
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.
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Govindamoorthi et al. (2022) studied Atherosclerosis. Maximum Likelihood Swarm Whale Optimization (MLSWO) with LeNet classifier vs. Other state-of-art techniques was evaluated on Accuracy, sensitivity, and specificity of disease prediction. The proposed Maximum Likelihood Swarm Whale Optimization (MLSWO) algorithm with LeNet classifier achieved an accuracy rate of 99.2%, sensitivity of 98.36%, and specificity of 100% for predicting atherosclerosis.
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