Soumya Das, Monalisa Nayak, M. Senapati, Jeetamitra Satapathy
{"title":"基于速度增强鲸鱼优化算法的医疗数据分类","authors":"Soumya Das, Monalisa Nayak, M. Senapati, Jeetamitra Satapathy","doi":"10.1109/icacfct53978.2021.9837345","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":312952,"journal":{"name":"2021 First International Conference on Advances in Computing and Future Communication Technologies (ICACFCT)","volume":"73 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Medical Data Classification Using Velocity Enhanced Whale Optimization Algorithm\",\"authors\":\"Soumya Das, Monalisa Nayak, M. Senapati, Jeetamitra Satapathy\",\"doi\":\"10.1109/icacfct53978.2021.9837345\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"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.\",\"PeriodicalId\":312952,\"journal\":{\"name\":\"2021 First International Conference on Advances in Computing and Future Communication Technologies (ICACFCT)\",\"volume\":\"73 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-12-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2021 First International Conference on Advances in Computing and Future Communication Technologies (ICACFCT)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/icacfct53978.2021.9837345\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 First International Conference on Advances in Computing and Future Communication Technologies (ICACFCT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/icacfct53978.2021.9837345","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
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.