A. Akhila, K. Hemalatha, A. Navya, B. Tejaswi, K. Hemanth, Ramesh Alladi
{"title":"AUTOMATIC MULTI-DISEASES PREDICTION USING MACHINE LEARNING","authors":"A. Akhila, K. Hemalatha, A. Navya, B. Tejaswi, K. Hemanth, Ramesh Alladi","doi":"10.54473/ijtret.2022.6308","DOIUrl":null,"url":null,"abstract":"Disease prediction has become one of the most difficult challenges in medicine in recent years. To eliminate the hazards connected with prediction, it is necessary to automate the process and notify the patient well in advance. The medical database is mostly made up of discrete data. As a result, making decisions using discrete data is a difficult task. Machine learning simplifies the process. The major purpose of this research is to give doctors a tool to diagnose diseases in their early stages. This model includes a user interface that allows users to anticipate ailments such as heart disease, Parkinson's disease, cancer, and diabetes. We utilized SVM and Logistic Regression for classification.","PeriodicalId":127327,"journal":{"name":"International Journal Of Trendy Research In Engineering And Technology","volume":"230 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal Of Trendy Research In Engineering And Technology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.54473/ijtret.2022.6308","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
Disease prediction has become one of the most difficult challenges in medicine in recent years. To eliminate the hazards connected with prediction, it is necessary to automate the process and notify the patient well in advance. The medical database is mostly made up of discrete data. As a result, making decisions using discrete data is a difficult task. Machine learning simplifies the process. The major purpose of this research is to give doctors a tool to diagnose diseases in their early stages. This model includes a user interface that allows users to anticipate ailments such as heart disease, Parkinson's disease, cancer, and diabetes. We utilized SVM and Logistic Regression for classification.