{"title":"基于机器学习算法的心脏病自动诊断系统的发展:现状与展望","authors":"Somnath B. Thigale et al.","doi":"10.52783/cienceng.v11i1.189","DOIUrl":null,"url":null,"abstract":"Nowadays Heart disease is one of the most common and serious disease as it is the one of major cause of death globally. Heart disease prediction is very critical and challenging task. Machine Learning (ML) an important technique in the field of Health care applications. Such systems assist (Not replaces) doctors in the interpretation of diseases. Automated Heart Disease Diagnosis System using Machine Learning Algorithm is amongst popular systems which have attracted the attention of numerous researchers, making it a thrust area for further investigations. In last decade, extensive investigations have been contributed to design Automated Heart Disease Diagnosis System using Machine Learning Algorithm. Designing a heart disease diagnosis system with high accuracy can help to save lives. A comprehensive survey of the developments and current trends in area of Automated Heart Disease Diagnosis System using Machine Learning algorithms is presented in this paper. The survey details the overall advancements in diagnosis or prediction of heart disease and an effective review of pre-processing of data, feature extraction algorithms and the classifiers used in prediction system. Few unaddressed issues and challenges that have comparatively received meagre attention are discussed highlighting the future prospects of Heart Disease Diagnosis and providing pointers to the further research.","PeriodicalId":214525,"journal":{"name":"Proceeding International Conference on Science and Engineering","volume":"31 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Development of Automated Heart Disease Diagnosis System Using Machine Learning Algorithm: Current Status And Future Prospects\",\"authors\":\"Somnath B. Thigale et al.\",\"doi\":\"10.52783/cienceng.v11i1.189\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Nowadays Heart disease is one of the most common and serious disease as it is the one of major cause of death globally. Heart disease prediction is very critical and challenging task. Machine Learning (ML) an important technique in the field of Health care applications. Such systems assist (Not replaces) doctors in the interpretation of diseases. Automated Heart Disease Diagnosis System using Machine Learning Algorithm is amongst popular systems which have attracted the attention of numerous researchers, making it a thrust area for further investigations. In last decade, extensive investigations have been contributed to design Automated Heart Disease Diagnosis System using Machine Learning Algorithm. Designing a heart disease diagnosis system with high accuracy can help to save lives. A comprehensive survey of the developments and current trends in area of Automated Heart Disease Diagnosis System using Machine Learning algorithms is presented in this paper. The survey details the overall advancements in diagnosis or prediction of heart disease and an effective review of pre-processing of data, feature extraction algorithms and the classifiers used in prediction system. Few unaddressed issues and challenges that have comparatively received meagre attention are discussed highlighting the future prospects of Heart Disease Diagnosis and providing pointers to the further research.\",\"PeriodicalId\":214525,\"journal\":{\"name\":\"Proceeding International Conference on Science and Engineering\",\"volume\":\"31 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-02-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceeding International Conference on Science and Engineering\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.52783/cienceng.v11i1.189\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceeding International Conference on Science and Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.52783/cienceng.v11i1.189","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Development of Automated Heart Disease Diagnosis System Using Machine Learning Algorithm: Current Status And Future Prospects
Nowadays Heart disease is one of the most common and serious disease as it is the one of major cause of death globally. Heart disease prediction is very critical and challenging task. Machine Learning (ML) an important technique in the field of Health care applications. Such systems assist (Not replaces) doctors in the interpretation of diseases. Automated Heart Disease Diagnosis System using Machine Learning Algorithm is amongst popular systems which have attracted the attention of numerous researchers, making it a thrust area for further investigations. In last decade, extensive investigations have been contributed to design Automated Heart Disease Diagnosis System using Machine Learning Algorithm. Designing a heart disease diagnosis system with high accuracy can help to save lives. A comprehensive survey of the developments and current trends in area of Automated Heart Disease Diagnosis System using Machine Learning algorithms is presented in this paper. The survey details the overall advancements in diagnosis or prediction of heart disease and an effective review of pre-processing of data, feature extraction algorithms and the classifiers used in prediction system. Few unaddressed issues and challenges that have comparatively received meagre attention are discussed highlighting the future prospects of Heart Disease Diagnosis and providing pointers to the further research.