Axel Frederick Félix Jiménez, Isaul Ibarra Belmonte, Vania Stephany Sánchez Lee, Uziel Jaramillo Avila, Ezra Federico Parra González
{"title":"Development of a deep learning system for preventive detection of heart failure due to pulmonary disease","authors":"Axel Frederick Félix Jiménez, Isaul Ibarra Belmonte, Vania Stephany Sánchez Lee, Uziel Jaramillo Avila, Ezra Federico Parra González","doi":"10.1109/CIMPS57786.2022.10035688","DOIUrl":null,"url":null,"abstract":"In the context of addressing the problem of people who do not undergo a diagnosis of heart failure due to pulmonary conditions on time, a solution to this problem would allow early preventive detection to avoid the development of severe disease efficiently. Our approach employs the use of medical data retrieved from the user to determine and predict whether there is a likelihood of a potential condition. To solve this problem, according to a users medical measurement history, a deep learning model can be implemented to determine a preventive diagnosis or otherwise to follow up on an already detected condition. By posing the problem as a classification task, it can be taken advantage of a deep learning model focused on heart failure or pulmonary conditions to make a preliminary diagnosis and determine if there are signs of any symptomatology.","PeriodicalId":205829,"journal":{"name":"2022 11th International Conference On Software Process Improvement (CIMPS)","volume":"86 6 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 11th International Conference On Software Process Improvement (CIMPS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CIMPS57786.2022.10035688","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 the context of addressing the problem of people who do not undergo a diagnosis of heart failure due to pulmonary conditions on time, a solution to this problem would allow early preventive detection to avoid the development of severe disease efficiently. Our approach employs the use of medical data retrieved from the user to determine and predict whether there is a likelihood of a potential condition. To solve this problem, according to a users medical measurement history, a deep learning model can be implemented to determine a preventive diagnosis or otherwise to follow up on an already detected condition. By posing the problem as a classification task, it can be taken advantage of a deep learning model focused on heart failure or pulmonary conditions to make a preliminary diagnosis and determine if there are signs of any symptomatology.