基于速度增强鲸鱼优化算法的医疗数据分类

Soumya Das, Monalisa Nayak, M. Senapati, Jeetamitra Satapathy
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引用次数: 2

摘要

这种疾病的早期诊断有助于患者度过难关。医疗数据集的诊断是通过不同的机器学习方法进行的。本文采用速度增强鲸鱼优化算法(Velocity Enhanced Whale Optimization Algorithm, VEWOA)作为分类算法,对心脏病、超声心动图、肝炎等三种不同类型的医疗数据集进行预测,并与鲸鱼优化算法(Whale Optimization Algorithm, WOA)进行比较。VEWOA-NN在心脏病数据集的分类准确率为97.2%,在超声心动图数据集的分类准确率为96%,在肝炎数据集的分类准确率为92.5%。性能验证是借助均方根误差(RMSE)和时间完成的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Medical Data Classification Using Velocity Enhanced Whale Optimization Algorithm
Early diagnosis of the disease helps the patients to get through the disease. Diagnosis of medical datasets are carried out by different machine learning approaches. In this paper, Velocity Enhanced Whale Optimization Algorithm (VEWOA) is taken as classification algorithm to predict three different varieties of medical datasets like Heart disease, Echocardiogram and Hepatitis and then compared with Whale Optimization Algorithm (WOA). The classification accuracy of VEWOA-NN is 97.2 percentage in heart disease dataset, 96 percentage in echo-cardiogram dataset and 92.5 percentage in Hepatitis dataset. The performance validation is done with the help of Root Mean Square Error (RMSE) and time.
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