Mohamed G. El-Shafiey, Ahmed M. Hagag, E. El-Dahshan, Manal A. Ismail
{"title":"Heart-Disease Prediction Method Using Random Forest and Genetic Algorithms","authors":"Mohamed G. El-Shafiey, Ahmed M. Hagag, E. El-Dahshan, Manal A. Ismail","doi":"10.1109/ICEEM52022.2021.9480625","DOIUrl":null,"url":null,"abstract":"Today, heart-disease is one of the most significant causes of mortality in the world. Thus, the prediction of heart-disease is a critical challenge in the area of healthcare systems. In this study, we aim to select the optimal features that can increase the accuracy of heart-disease prediction. A feature-selection algorithm, which is based on genetic algorithm (GA) and random forest (RF), is proposed to increase the accuracy of RF-based classification and determine the optimal heart-disease-prediction features. The performance of the proposed approach is validated via evaluation metrics, namely, accuracy, specificity, sensitivity, and area under the ROC curve by using a public dataset from the University of California, namely, Cleveland. The experimental results confirm that the proposed approach attained the high heart-disease-prediction accuracies of 95.6% on the Cleveland dataset. Furthermore, the proposed approach outperformed other state-of-the-art prediction methods.","PeriodicalId":352371,"journal":{"name":"2021 International Conference on Electronic Engineering (ICEEM)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 International Conference on Electronic Engineering (ICEEM)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICEEM52022.2021.9480625","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Today, heart-disease is one of the most significant causes of mortality in the world. Thus, the prediction of heart-disease is a critical challenge in the area of healthcare systems. In this study, we aim to select the optimal features that can increase the accuracy of heart-disease prediction. A feature-selection algorithm, which is based on genetic algorithm (GA) and random forest (RF), is proposed to increase the accuracy of RF-based classification and determine the optimal heart-disease-prediction features. The performance of the proposed approach is validated via evaluation metrics, namely, accuracy, specificity, sensitivity, and area under the ROC curve by using a public dataset from the University of California, namely, Cleveland. The experimental results confirm that the proposed approach attained the high heart-disease-prediction accuracies of 95.6% on the Cleveland dataset. Furthermore, the proposed approach outperformed other state-of-the-art prediction methods.