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Modeling Attention Performance Across Female Reproductive Aging Using Logistic Regression

Authors :

Zahra Zehtabi¹ Leila Mehdizadeh Fanid² Pedram Salehpoor³ Mahdi Jafari Asl⁴

1- دانشگاه تبریز 2- دانشگاه تبریز 3- دانشگاه تبریز 4- دانشگاه شاهد-تهران

Keywords :

Attention،Reproductive Aging،Logistic Regression،Machine Learning،MoCA،IVA-2

Abstract :

Reproductive aging in women is associated with cognitive decline, particularly in attention domains. This study investigates the use of machine learning, specifically logistic regression, to predict attentional performance during different reproductive aging stages. A total of 100 healthy women, 50 premenopausal (mean age = 29.5) and 50 postmenopausal (mean age = 54.8), underwent neuropsychological evaluation using the Montreal Cognitive Assessment (MoCA) and the Integrated Visual and Auditory (IVA-2) test. Among the applied supervised models, logistic regression achieved 95.0% accuracy in 10-fold cross-validation and 100% on the test set. The analysis revealed a strong inverse correlation between reproductive aging and auditory attention subscores. These results suggest that logistic regression offers a reliable, interpretable, and clinically applicable model for early detection of attention decline, supporting its integration into biomedical screening tools.