Construction and Comparison of Machine Learning-Based Risk Prediction Models for Major Adverse Cardiovascular Events in Perimenopausal Women.

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, =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.