The XGBoost model demonstrated the highest predictive value for coronary heart disease in young and middle-aged patients, achieving an AUC of 0.940 and an F1 score of 0.887.
Cohort (n=553)
No
Do machine learning models accurately predict coronary heart disease risk in young and middle-aged patients?
An XGBoost machine learning model can highly accurately predict the risk of coronary heart disease in young and middle-aged individuals, potentially aiding in clinical screening.
Absolute Event Rate: 0.94% vs 0.829%
Objective To identify coronary heart disease risk factors in young and middle-aged persons and develop a tailored risk prediction model. Methods A retrospective cohort study was used in this research. From January 2017 to January 2020, 553 patients in the Department of Cardiology at a tertiary hospital in Anhui Province were chosen as research subjects. The research subjects were separated into two groups based on the results of coronary angiography performed during hospitalization ( n = 201) and non-coronary heart disease ( n = 352). R software (R 3.6.1) was used to analyze the clinical data of the two groups. A logistic regression prediction model and three machine learning models, including BP neural network, Extreme gradient boosting (XGBoost), and random forest, were built, and the best prediction model was chosen based on the relevant parameters of the different machine learning models. Results Univariate analysis identified a total of 24 indexes with statistically significant differences between coronary heart disease and non-coronary heart disease groups, which were incorporated in the logistic regression model and three machine learning models. The AUCs of the test set in the logistic regression prediction model, BP neural network model, random forest model, and XGBoost model were 0.829, 0.795, 0.928, and 0.940, respectively, and the F1 scores were 0.634, 0.606, 0.846, and 0.887, indicating that the XGBoost model’s prediction value was the best. Conclusion The XGBoost model, which is based on coronary heart disease risk factors in young and middle-aged people, has a high risk prediction efficiency for coronary heart disease in young and middle-aged people and can help clinical medical staff screen young and middle-aged people at high risk of coronary heart disease in clinical practice.
Cao et al. (Wed,) conducted a cohort in Coronary heart disease (n=553). XGBoost model vs. Logistic regression, BP neural network, and random forest models was evaluated on Area under the curve (AUC) for predicting coronary heart disease. The XGBoost model demonstrated the highest predictive value for coronary heart disease in young and middle-aged patients, achieving an AUC of 0.940 and an F1 score of 0.887.