The XGBoost machine learning model effectively predicted atrial fibrillation in elderly patients with coronary heart disease and type 2 diabetes mellitus, achieving an AUC of 0.743 compared to 0.684 for logistic regression.
Observational (n=3,858)
Yes
Do machine learning algorithms accurately predict atrial fibrillation in hospitalized elderly patients with coronary heart disease and type 2 diabetes mellitus?
Machine learning models, particularly Random Forest and XGBoost, can effectively predict atrial fibrillation in elderly patients with coronary heart disease and type 2 diabetes using routine admission laboratory values like total bilirubin, triglycerides, and uric acid.
Effect estimate: AUC 0.743 (95% CI 0.693-0.792)
Absolute Event Rate: 0.743% vs 0.684%
Background: The objective of this study was to use machine learning algorithms to construct predictive models for atrial fibrillation (AF) in elderly patients with coronary heart disease (CHD) and type 2 diabetes mellitus (T2DM). Methods: The diagnosis and treatment data of elderly patients with CHD and T2DM, who were treated in four tertiary hospitals in Chongqing, China from 2015 to 2021, were collected. Five machine learning algorithms: logistic regression, logistic regression+least absolute shrinkage and selection operator, classified regression tree (CART), random forest (RF) and extreme gradient lifting (XGBoost) were used to construct the prediction models. The area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and accuracy were used as the comparison measures between different models. Results: A total of 3,858 elderly patients with CHD and T2DM were included. In the internal validation cohort, XGBoost had the highest AUC (0.743) and sensitivity (0.833), and RF had the highest specificity (0.753) and accuracy (0.735). In the external verification, RF had the highest AUC (0.726) and sensitivity (0.686), and CART had the highest specificity (0.925) and accuracy (0.841). Total bilirubin, triglycerides and uric acid were the three most important predictors of AF. Conclusion: The risk prediction models of AF in elderly patients with CHD and T2DM based on machine learning algorithms had high diagnostic value. The prediction models constructed by RF and XGBoost were more effective. The results of this study can provide reference for the clinical prevention and treatment of AF.
Xu et al. (Fri,) conducted a observational in Coronary Heart Disease and Type 2 Diabetes Mellitus (n=3,858). XGBoost machine learning model vs. Logistic regression was evaluated on Area under the receiver operating characteristic curve (AUC) for predicting atrial fibrillation in the internal validation cohort (AUC 0.743, 95% CI 0.693-0.792). The XGBoost machine learning model effectively predicted atrial fibrillation in elderly patients with coronary heart disease and type 2 diabetes mellitus, achieving an AUC of 0.743 compared to 0.684 for logistic regression.
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