Alina Faskhutdinova, Daria Grigorieva, Bulat Garafutdinov, V. Mokshin
{"title":"脑卒中预测的机器学习方法研究","authors":"Alina Faskhutdinova, Daria Grigorieva, Bulat Garafutdinov, V. Mokshin","doi":"10.1109/ITNT57377.2023.10139121","DOIUrl":null,"url":null,"abstract":"This article discusses methods for predicting stroke. It has been shown that there are different methods for solving the problem. The article presents a description of the developed model for predicting the likelihood of stroke. The system allows for a quick diagnosis of this disease based on a small number of input parameters. Several methods for implementing machine learning have been considered. The method of support vectors SVM (Support Vector Machine) was taken as a basis.","PeriodicalId":296438,"journal":{"name":"2023 IX International Conference on Information Technology and Nanotechnology (ITNT)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Investigation of Machine Learning Methods for Stroke Prediction\",\"authors\":\"Alina Faskhutdinova, Daria Grigorieva, Bulat Garafutdinov, V. Mokshin\",\"doi\":\"10.1109/ITNT57377.2023.10139121\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This article discusses methods for predicting stroke. It has been shown that there are different methods for solving the problem. The article presents a description of the developed model for predicting the likelihood of stroke. The system allows for a quick diagnosis of this disease based on a small number of input parameters. Several methods for implementing machine learning have been considered. The method of support vectors SVM (Support Vector Machine) was taken as a basis.\",\"PeriodicalId\":296438,\"journal\":{\"name\":\"2023 IX International Conference on Information Technology and Nanotechnology (ITNT)\",\"volume\":\"2 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-04-17\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2023 IX International Conference on Information Technology and Nanotechnology (ITNT)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ITNT57377.2023.10139121\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2023 IX International Conference on Information Technology and Nanotechnology (ITNT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ITNT57377.2023.10139121","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Investigation of Machine Learning Methods for Stroke Prediction
This article discusses methods for predicting stroke. It has been shown that there are different methods for solving the problem. The article presents a description of the developed model for predicting the likelihood of stroke. The system allows for a quick diagnosis of this disease based on a small number of input parameters. Several methods for implementing machine learning have been considered. The method of support vectors SVM (Support Vector Machine) was taken as a basis.