Exploring Brain Structure Differences and Male/Female Classification: A Machine Learning Study on Huffaz and Non-Huffaz

This study investigates the potential of machine learning (ML) to classify sex based on brain structural features, incorporating the unique perspective of cognitive training through Quran memorization. Using T1-weighted MRI scans from 47 healthy young adults (23 Huffaz, 24 non-Huffaz), we extracted features related to brain volume and fractal dimensions (box-counting and Fourier). Four ML algorithms (logistic regression, support vector machine, deep learning, random forest) were employed to classify sex, with Huffaz/non-Huffaz status as a predictor. Results showed high accuracy (up to 92.86%) with random forest and deep learning, particularly when combining multiple features. This suggests the potential of brain structure as a sexual biomarker and highlights the impact of cognitive training on brain morphology. Further research with larger, diverse cohorts is needed to validate these findings and explore the specific neural correlates underlying sex differences and the influence of cognitive practices.