Key result
The Random Forest model demonstrated superior performance in predicting the risk of major adverse cardiovascular events in perimenopausal women with an AUC of 0.948, significantly outperforming the logistic regression model (AUC 0.866, p=0.023).
Why the study?
Ovarian function decline and lower estrogen levels in perimenopausal women may lead to cardiovascular disease, motivating exploration of factors influencing MACE and risk prediction model development.
Does a Random Forest machine learning model improve the prediction of Major Adverse Cardiovascular Events in perimenopausal women compared to logistic regression?
Case-Control (n=411)
No
Does a Random Forest machine learning model improve the prediction of Major Adverse Cardiovascular Events in perimenopausal women compared to logistic regression?
Absolute Event Rate: 0.948% vs 0.866%
p-value: p=0.023
A Random Forest machine learning model demonstrated high accuracy and outperformed logistic regression in predicting the risk of major adverse cardiovascular events in perimenopausal women.
RF models may aid early MACE risk identification in perimenopausal women; leaves open prospective validation before clinical use.
Background: Perimenopausal period is a period of physiological changes in women with signs of ovarian failure, including menopausal transition period and 1 year after menopause. Ovarian function declines in perimenopausal women and lower estrogen levels lead to changes in the function of various organs, which may lead to cardiovascular disease. Major adverse cardiovascular events (MACE) are the combination of clinical events including heart failure, myocardial infarction and other cardiovascular diseases. Therefore, this study explores the factors influencing the occurrence of MACE in perimenopausal women and establishes a prediction model for MACE risk factors using three algorithms, comparing their predictive performance. Patients and Methods: A total of 411 perimenopausal women diagnosed with MACE at the Binzhou Medical University Hospital were randomly divided into a training set and a test set following a 7:3 ratio. According to the principle of 10 events per Variable, the training set sample size was sufficient. In the training set, Random Forest (RF) algorithm, backpropagation neural network (BPNN) and Logistic Regression (LR) were used to construct a MACE risk prediction model for perimenopausal women, and the test set was used to verify the model. The prediction performance of the model was evaluated in terms of accuracy, sensitivity, specificity, and area under the subject operating characteristic curve (AUC). Results: A total of twenty-six candidate variables were included. The area under ROC curve of the RF model, BPNN model, and logistic regression model was 0.948, 0.921, and 0.866. Comparison of ROC curve AUC between logistic regression and RF model for predicting MACE risk showed a statistically significant difference (Z=2.278, P =0.023). Conclusion: The RF model showed good performance in predicting the risk of MACE in perimenopausal women providing a reference for the early identification of high-risk patients and the development of targeted intervention strategies.
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Chen et al. (2025) conducted a case-control in Major Adverse Cardiovascular Events (MACE) in perimenopausal women (n=411). Random Forest (RF) model vs. Logistic Regression (LR) model was evaluated on Area under the ROC curve (AUC) for predicting MACE risk (95% CI 0.925-0.971, p=0.023). The Random Forest model demonstrated superior performance in predicting the risk of major adverse cardiovascular events in perimenopausal women with an AUC of 0.948, significantly outperforming the logistic regression model (AUC 0.866, p=0.023).
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