Brain and behavior
Ensemble-Based Machine Learning Models for Classifying Intervention-Related Psychological Outcome Patterns in Menopausal Women Treated With Pimpinella anisum
By Heyat MBB, Sultana A, Rehman AU and 2 others.
Abstract
Background
Menopause, a natural biological transition in the life of women, is often associated with significant psychological disturbances such as depression, anxiety, and stress. However, early identification and management of psychological disorders in menopausal women remain under-addressed due to limited specialist access and a lack of systematic screening approaches.
Purpose
This study aimed to develop ensemble-based machine learning (ML) models to classify intervention-related clinical and psychological outcome patterns in menopausal women using clinical and questionnaire-based data collected during a controlled intervention study.
Method
Psychological and menopausal symptoms were assessed using standardized tools, including the Depression Anxiety Stress Scale-21 (DASS-21), modified Kupperman index (MKI), vaginal health index (VHI), and menopause symptoms treatment satisfaction questionnaire (MS-TSQ). Statistical analyzes were performed using chi-square tests and independent t-tests to evaluate group differences. Three ensemble classifiers, random forest (RF), gradient boosting (GB), and AdaBoost (AB), were evaluated using repeated cross-validation.