{"title":"心血管疾病机器学习预测中的多模式特征集成","authors":"Nandhini G, S. Balivada","doi":"10.60142/ijhti.v2i03.03","DOIUrl":null,"url":null,"abstract":"Early detection and prevention of cardiovascular illnesses rely heavily on phonocardiogram (PCG) and electrocardiogram (ECG). A novel multi-modal machine learning strategy based on ECG and PCG data is presented in this work for predicting cardiovascular diseases (CVD). ECG and PCG features are combined for optimal feature subset selection using a genetic algorithm (GA). Then, machine learning classifiers are implemented to do the classification of abnormal and normal signals","PeriodicalId":324941,"journal":{"name":"International Journal of Health Technology and Innovation","volume":"45 8","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2023-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Multi-Modal Feature Integration in Machine Learning Predictions for Cardiovascular Diseases\",\"authors\":\"Nandhini G, S. Balivada\",\"doi\":\"10.60142/ijhti.v2i03.03\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Early detection and prevention of cardiovascular illnesses rely heavily on phonocardiogram (PCG) and electrocardiogram (ECG). A novel multi-modal machine learning strategy based on ECG and PCG data is presented in this work for predicting cardiovascular diseases (CVD). ECG and PCG features are combined for optimal feature subset selection using a genetic algorithm (GA). Then, machine learning classifiers are implemented to do the classification of abnormal and normal signals\",\"PeriodicalId\":324941,\"journal\":{\"name\":\"International Journal of Health Technology and Innovation\",\"volume\":\"45 8\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-12-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of Health Technology and Innovation\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.60142/ijhti.v2i03.03\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Health Technology and Innovation","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.60142/ijhti.v2i03.03","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Multi-Modal Feature Integration in Machine Learning Predictions for Cardiovascular Diseases
Early detection and prevention of cardiovascular illnesses rely heavily on phonocardiogram (PCG) and electrocardiogram (ECG). A novel multi-modal machine learning strategy based on ECG and PCG data is presented in this work for predicting cardiovascular diseases (CVD). ECG and PCG features are combined for optimal feature subset selection using a genetic algorithm (GA). Then, machine learning classifiers are implemented to do the classification of abnormal and normal signals
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