Daria Grigorieva, Alina Faskhutdinova, Bulat Garafutdinov, V. Mokshin
{"title":"Researching Machine Learning Methods for Preventing Cardiovascular Diseases","authors":"Daria Grigorieva, Alina Faskhutdinova, Bulat Garafutdinov, V. Mokshin","doi":"10.1109/ITNT57377.2023.10139052","DOIUrl":null,"url":null,"abstract":"In this article, a review of existing methods for developing a model for the prevention of cardiovascular diseases was carried out, their advantages and disadvantages were identified. Mortality and morbidity from heart disease has been leading in recent decades throughout the world. The use of various machine learning algorithms, including deep learning algorithms, significantly improves the accuracy of predicting cardiovascular risks of trained models. Using the data obtained, we created a model with which we can identify a group of people who are more at risk of heart disease.","PeriodicalId":296438,"journal":{"name":"2023 IX International Conference on Information Technology and Nanotechnology (ITNT)","volume":"44 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/ITNT57377.2023.10139052","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
In this article, a review of existing methods for developing a model for the prevention of cardiovascular diseases was carried out, their advantages and disadvantages were identified. Mortality and morbidity from heart disease has been leading in recent decades throughout the world. The use of various machine learning algorithms, including deep learning algorithms, significantly improves the accuracy of predicting cardiovascular risks of trained models. Using the data obtained, we created a model with which we can identify a group of people who are more at risk of heart disease.