{"title":"心脏疾病预测的机器学习技术系统综述","authors":"Shivganga Udhan, B. Patil","doi":"10.47164/IJNGC.V12I2.783","DOIUrl":null,"url":null,"abstract":"Machine learning includes articial intelligence, which is implemented to solve a number of data science problems. The prediction of outcomes based on existing data is a common machine learning application.Different data mining strategies for the prediction of heart disease have been proposed with varying degrees of effectiveness and accuracy. In this paper, author provide an in-depth literature survey on systems for predicting risk of heart disease.","PeriodicalId":351421,"journal":{"name":"Int. J. Next Gener. Comput.","volume":"27 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"A systematic review of Machine learning techniques for Heart disease prediction\",\"authors\":\"Shivganga Udhan, B. Patil\",\"doi\":\"10.47164/IJNGC.V12I2.783\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Machine learning includes articial intelligence, which is implemented to solve a number of data science problems. The prediction of outcomes based on existing data is a common machine learning application.Different data mining strategies for the prediction of heart disease have been proposed with varying degrees of effectiveness and accuracy. In this paper, author provide an in-depth literature survey on systems for predicting risk of heart disease.\",\"PeriodicalId\":351421,\"journal\":{\"name\":\"Int. J. Next Gener. Comput.\",\"volume\":\"27 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-04-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Int. J. Next Gener. Comput.\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.47164/IJNGC.V12I2.783\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Int. J. Next Gener. Comput.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.47164/IJNGC.V12I2.783","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A systematic review of Machine learning techniques for Heart disease prediction
Machine learning includes articial intelligence, which is implemented to solve a number of data science problems. The prediction of outcomes based on existing data is a common machine learning application.Different data mining strategies for the prediction of heart disease have been proposed with varying degrees of effectiveness and accuracy. In this paper, author provide an in-depth literature survey on systems for predicting risk of heart disease.