Udaiyakumar Ramamoorthy, N. Vijayalakshmi, M. Prashanthram, S. Jayaprakash
{"title":"机器学习与人工神经网络算法的比较研究","authors":"Udaiyakumar Ramamoorthy, N. Vijayalakshmi, M. Prashanthram, S. Jayaprakash","doi":"10.1109/ICACCS48705.2020.9074203","DOIUrl":null,"url":null,"abstract":"The diagnosis of heart disease by classical medical approach takes huge amount of time. Besides blood tests and X-ray this approach includes multiple tests like MRI, Echocardiogram and whose results are prone to misdiagnosis. Our proposed model can predict whether a patient with given health parameters and certain test results is affected by a heart disease. The proposed model uses AI approach with several ML algorithms like KNN, SVM, Decision Tree, Random forest classifiers and also with deep neural networks. This prediction is done based on the historical data collected from different medical Institutes in Central Europe.","PeriodicalId":439003,"journal":{"name":"2020 6th International Conference on Advanced Computing and Communication Systems (ICACCS)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"A Comparative Study on Machine Learning and Artificial Neural Networking Algorithms\",\"authors\":\"Udaiyakumar Ramamoorthy, N. Vijayalakshmi, M. Prashanthram, S. Jayaprakash\",\"doi\":\"10.1109/ICACCS48705.2020.9074203\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The diagnosis of heart disease by classical medical approach takes huge amount of time. Besides blood tests and X-ray this approach includes multiple tests like MRI, Echocardiogram and whose results are prone to misdiagnosis. Our proposed model can predict whether a patient with given health parameters and certain test results is affected by a heart disease. The proposed model uses AI approach with several ML algorithms like KNN, SVM, Decision Tree, Random forest classifiers and also with deep neural networks. This prediction is done based on the historical data collected from different medical Institutes in Central Europe.\",\"PeriodicalId\":439003,\"journal\":{\"name\":\"2020 6th International Conference on Advanced Computing and Communication Systems (ICACCS)\",\"volume\":\"32 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-03-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 6th International Conference on Advanced Computing and Communication Systems (ICACCS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICACCS48705.2020.9074203\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 6th International Conference on Advanced Computing and Communication Systems (ICACCS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICACCS48705.2020.9074203","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A Comparative Study on Machine Learning and Artificial Neural Networking Algorithms
The diagnosis of heart disease by classical medical approach takes huge amount of time. Besides blood tests and X-ray this approach includes multiple tests like MRI, Echocardiogram and whose results are prone to misdiagnosis. Our proposed model can predict whether a patient with given health parameters and certain test results is affected by a heart disease. The proposed model uses AI approach with several ML algorithms like KNN, SVM, Decision Tree, Random forest classifiers and also with deep neural networks. This prediction is done based on the historical data collected from different medical Institutes in Central Europe.