Govindamoorthi P, Ranjith Kumar P
PSR Engineering Collège, Sivakasi, Tamil Nadu, India.
Comput Methods Biomech Biomed Engin. 2023 Feb;26(3):326-337. doi: 10.1080/10255842.2022.2116577. Epub 2022 Sep 1.
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.
由动脉粥样硬化引起的冠状动脉疾病（CAD）正在产生巨大影响，并且在全世界都被视为一种流行病。2019年心血管疾病（CVD）的记录约占全球死亡率的32%。在这些死亡中，85%是由心脏病发作和中风引起的。动脉粥样硬化被认为是一种由于动脉壁周围斑块积聚而导致动脉变硬和变窄的病症。该疾病发展缓慢，无症状，会导致心脏骤停、心肌梗死和中风。目前，医学诊断技术被广泛应用于疾病预测。然而，它们在检测所需的灵敏度和分辨率方面并不常见。缺乏用于早期疾病预测的非侵入性诊断工具限制了对不同程度患者的治疗和预防。这项拟议的研究工作侧重于一种名为最大似然群鲸优化（MLSWO）的智能优化技术，该技术用于提取动脉粥样硬化（STULONG）和Kaggle数据集中的关键特征并预测疾病进展。使用LeNet分类器对所选特征的结果进行分类，以对个体进行分类。与其他现有技术相比，所提出的MLSWO算法产生了99.2%的更高准确率、98.36%的灵敏度率和100%的特异性。
