Establishment of a diagnostic model of coronary heart disease in elderly patients with diabetes mellitus based on machine learning algorithms
Hu XU1
Wen-Zhe CAO2
Yong-Yi BAI3
Jing DONG4
He-Bin CHE4
Po BAI5
Jian-Dong WANG6
Feng CAO7
Li FAN7
1.Chinese PLA Medical School,Chinese PLA General Hospital,Beijing,China;Department of Cardiology,the Second Medical Center,National Clinical Research Center for Geriatric Diseases,Chinese PLA General Hospital,Beijing,China;Department of General Surgery,the First Medical Center,Chinese PLA General Hospital,Beijing,China2.Chinese PLA Medical School,Chinese PLA General Hospital,Beijing,China;Institute of Geriatrics,the Second Medical Center,Chinese PLA General Hospital,Beijing,China3.Department of Cardiology,the Second Medical Center,National Clinical Research Center for Geriatric Diseases,Chinese PLA General Hospital,Beijing,China4.Medical Big Data Research Center&National Engineering Laboratory for Medical Big Data Application Technology,Chinese PLA General Hospital,Beijing,China5.Department of Respiratory Diseases,Chinese PLA Rocket Force Characteristic Medical Center,Beijing,China6.Chinese PLA Medical School,Chinese PLA General Hospital,Beijing,China;Department of General Surgery,the First Medical Center,Chinese PLA General Hospital,Beijing,China7.Chinese PLA Medical School,Chinese PLA General Hospital,Beijing,China;Department of Cardiology,the Second Medical Center,National Clinical Research Center for Geriatric Diseases,Chinese PLA General Hospital,Beijing,China
摘要：OBJECTIVE To establish a prediction model of coronary heart disease (CHD) in elderly patients with diabetes mellitus (DM) ba-sed on machine learning (ML) algorithms. METHODS Based on the Medical Big Data Research Centre of Chinese PLA General Hospital in Beijing, China, we identified a cohort of elderly inpatients (≥ 60 years), including 10,533 patients with DM complicated with CHD and 12,634 patients with DM without CHD, from January 2008 to December 2017. We collected demographic characteristics and clinical data. After selecting the important features, we established five ML models, including extreme gradient boosting (XGBoost), random forest (RF), dec-ision tree (DT), adaptive boosting (Adaboost) and logistic regression (LR). We compared the receiver operating characteristic cur-ves, area under the curve (AUC) and other relevant parameters of different models and determined the optimal classification mo-del. The model was then applied to 7447 elderly patients with DM admitted from January 2018 to December 2019 to further valid-ate the performance of the model. RESULTS Fifteen features were selected and included in the ML model. The classification precision in the test set of the XG-Boost, RF, DT, Adaboost and LR models was 0.778, 0.789, 0.753, 0.750 and 0.689, respectively; and the AUCs of the subjects were 0.851, 0.845, 0.823, 0.833 and 0.731, respectively. Applying the XGBoost model with optimal performance to a newly recruited data-set for validation, the diagnostic sensitivity, specificity, precision, and AUC were 0.792, 0.808, 0.748 and 0.880, respectively. CONCLUSIONS The XGBoost model established in the present study had certain predictive value for elderly patients with DM complicated with CHD.
机标关键词：diagnosticlearningheartmodelwithalgorithmsbasedcoronary
论文发表日期：2022-06-28
在线出版日期：2025-08-15（本平台首次上网日期，不代表文献的发表时间）
页数：11( 445-455 )