The Random Forest model demonstrated superior predictive performance for premature coronary artery disease compared to the LASSO Logistic model, with an AUC of 0.91 versus 0.84 (P < 0.05).
Observational (n=797)
No
Does a Random Forest model improve the prediction of premature coronary artery disease compared to a LASSO Logistic Regression model in patients with coronary artery disease?
A Random Forest model demonstrated superior predictive performance compared to a LASSO Logistic Regression model for identifying premature coronary artery disease, highlighting hyperuricemia, chronic kidney disease, and carotid artery atherosclerosis as key predictors.
Absolute Event Rate: 0.91% vs 0.84%
p-value: p=<0.05
PURPOSE: With the change of lifestyle, the occurrence of coronary artery disease presents a younger trend, increasing the medical and economic burden on the family and society. To reduce the burden caused by this disease, this study applied LASSO Logistic Regression and Random Forest to establish a risk prediction model for premature coronary artery disease(PCAD) separately and compared the predictive performance of the two models. METHODS: The data are obtained from 1004 patients with coronary artery disease admitted to a third-class hospital in Liaoning Province from September 2019 to December 2021. The data from 797 patients were ultimately evaluated. The dataset of 797 patients was randomly divided into the training set (569 persons) and the validation set (228 persons) scale by 7:3. The risk prediction model was established and compared by LASSO Logistic and Random Forest. RESULT: The two models in this study showed that hyperuricemia, chronic renal disease, carotid artery atherosclerosis were important predictors of premature coronary artery disease. A result of the AUC between the two models showed statistical difference (Z = 3.47, P < 0.05). CONCLUSIONS: Random Forest has better prediction performance for PCAD and is suitable for clinical practice. It can provide an objective reference for the early screening and diagnosis of premature coronary artery disease, guide clinical decision-making and promote disease prevention.
Wang et al. (Wed,) conducted a observational in Premature coronary artery disease (PCAD) (n=797). Random Forest model vs. LASSO Logistic Regression model was evaluated on Area Under the ROC Curve (AUC) for predicting PCAD (p=<0.05). The Random Forest model demonstrated superior predictive performance for premature coronary artery disease compared to the LASSO Logistic model, with an AUC of 0.91 versus 0.84 (P < 0.05).