Jiajia Zhang, Heng Zhang, Ting Wei, Pinfang Kang, Bi Tang, Hongju Wang
{"title":"利用机器学习和高频 QRS 预测血管造影冠状动脉疾病。","authors":"Jiajia Zhang, Heng Zhang, Ting Wei, Pinfang Kang, Bi Tang, Hongju Wang","doi":"10.1186/s12911-024-02620-1","DOIUrl":null,"url":null,"abstract":"<p><strong>Aim: </strong>Exercise stress ECG is a common diagnostic test for stable coronary artery disease, but its sensitivity and specificity need to be further improved. In this paper, we construct a machine learning model for the prediction of angiographic coronary artery disease by HFQRS analysis of cycling exercise ECG.</p><p><strong>Methods and results: </strong>This study prospectively included 140 inpatients and 59 healthy volunteers undergoing cycling exercise ECG. The CHD group (N=104) and non-CHD group (N=95) were determined by coronary angiography gold standard. Automated HF QRS analysis was performed by the blinded method. The coronary group was predominantly male, with a higher prevalence of age, BMI, hypertension, and diabetes than the non-coronary group ( <math><mrow><mi>P</mi> <mo><</mo> <mn>0.001</mn></mrow> </math> ), higher lipid levels in the coronary group ( <math><mrow><mi>P</mi> <mo><</mo> <mn>0.005</mn></mrow> </math> ), significantly longer QRS duration during exercise testing ( <math><mrow><mi>P</mi> <mo><</mo> <mn>0.005</mn></mrow> </math> ), more positive leads ( <math><mrow><mi>P</mi> <mo><</mo> <mn>0.001</mn></mrow> </math> ), and a greater proportion of significant changes in HFQRS ( <math><mrow><mi>P</mi> <mo><</mo> <mn>0.001</mn></mrow> </math> ). Age, Gender, Hypertension, Diabetes, and HF QRS Conclusions were screened by correlation analysis and multifactorial retrospective analysis to construct the machine learning models of the XGBoost Classifier, Logistic Regression, LightGBM Classifier, RandomForest Classifier, Artificial Neural Network and Support Vector Machine, respectively.</p><p><strong>Conclusion: </strong>Male, elderly, with hypertension, diabetes mellitus, and positive exercise stress test HFQRS conclusions suggested a high risk of CHD. The best performance of the Logistic Regression model was compared, and a column line graph for assessing the risk of CHD was further developed and validated.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":null,"pages":null},"PeriodicalIF":3.3000,"publicationDate":"2024-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11292994/pdf/","citationCount":"0","resultStr":"{\"title\":\"Predicting angiographic coronary artery disease using machine learning and high-frequency QRS.\",\"authors\":\"Jiajia Zhang, Heng Zhang, Ting Wei, Pinfang Kang, Bi Tang, Hongju Wang\",\"doi\":\"10.1186/s12911-024-02620-1\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Aim: </strong>Exercise stress ECG is a common diagnostic test for stable coronary artery disease, but its sensitivity and specificity need to be further improved. In this paper, we construct a machine learning model for the prediction of angiographic coronary artery disease by HFQRS analysis of cycling exercise ECG.</p><p><strong>Methods and results: </strong>This study prospectively included 140 inpatients and 59 healthy volunteers undergoing cycling exercise ECG. The CHD group (N=104) and non-CHD group (N=95) were determined by coronary angiography gold standard. Automated HF QRS analysis was performed by the blinded method. The coronary group was predominantly male, with a higher prevalence of age, BMI, hypertension, and diabetes than the non-coronary group ( <math><mrow><mi>P</mi> <mo><</mo> <mn>0.001</mn></mrow> </math> ), higher lipid levels in the coronary group ( <math><mrow><mi>P</mi> <mo><</mo> <mn>0.005</mn></mrow> </math> ), significantly longer QRS duration during exercise testing ( <math><mrow><mi>P</mi> <mo><</mo> <mn>0.005</mn></mrow> </math> ), more positive leads ( <math><mrow><mi>P</mi> <mo><</mo> <mn>0.001</mn></mrow> </math> ), and a greater proportion of significant changes in HFQRS ( <math><mrow><mi>P</mi> <mo><</mo> <mn>0.001</mn></mrow> </math> ). Age, Gender, Hypertension, Diabetes, and HF QRS Conclusions were screened by correlation analysis and multifactorial retrospective analysis to construct the machine learning models of the XGBoost Classifier, Logistic Regression, LightGBM Classifier, RandomForest Classifier, Artificial Neural Network and Support Vector Machine, respectively.</p><p><strong>Conclusion: </strong>Male, elderly, with hypertension, diabetes mellitus, and positive exercise stress test HFQRS conclusions suggested a high risk of CHD. The best performance of the Logistic Regression model was compared, and a column line graph for assessing the risk of CHD was further developed and validated.</p>\",\"PeriodicalId\":9340,\"journal\":{\"name\":\"BMC Medical Informatics and Decision Making\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":3.3000,\"publicationDate\":\"2024-07-31\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11292994/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"BMC Medical Informatics and Decision Making\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1186/s12911-024-02620-1\",\"RegionNum\":3,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"MEDICAL INFORMATICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"BMC Medical Informatics and Decision Making","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1186/s12911-024-02620-1","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"MEDICAL INFORMATICS","Score":null,"Total":0}
Predicting angiographic coronary artery disease using machine learning and high-frequency QRS.
Aim: Exercise stress ECG is a common diagnostic test for stable coronary artery disease, but its sensitivity and specificity need to be further improved. In this paper, we construct a machine learning model for the prediction of angiographic coronary artery disease by HFQRS analysis of cycling exercise ECG.
Methods and results: This study prospectively included 140 inpatients and 59 healthy volunteers undergoing cycling exercise ECG. The CHD group (N=104) and non-CHD group (N=95) were determined by coronary angiography gold standard. Automated HF QRS analysis was performed by the blinded method. The coronary group was predominantly male, with a higher prevalence of age, BMI, hypertension, and diabetes than the non-coronary group ( ), higher lipid levels in the coronary group ( ), significantly longer QRS duration during exercise testing ( ), more positive leads ( ), and a greater proportion of significant changes in HFQRS ( ). Age, Gender, Hypertension, Diabetes, and HF QRS Conclusions were screened by correlation analysis and multifactorial retrospective analysis to construct the machine learning models of the XGBoost Classifier, Logistic Regression, LightGBM Classifier, RandomForest Classifier, Artificial Neural Network and Support Vector Machine, respectively.
Conclusion: Male, elderly, with hypertension, diabetes mellitus, and positive exercise stress test HFQRS conclusions suggested a high risk of CHD. The best performance of the Logistic Regression model was compared, and a column line graph for assessing the risk of CHD was further developed and validated.
期刊介绍:
BMC Medical Informatics and Decision Making is an open access journal publishing original peer-reviewed research articles in relation to the design, development, implementation, use, and evaluation of health information technologies and decision-making for human health.