Gender-Aware ADHD Detection Framework Combining XGBoost and FLAML Models: Exploring Predictive Features in Women Advancing Personalized ADHD Diagnosis

A machine learning architecture is introduced to predict attention deficit hyperactivity disorder (ADHD) and biological sex from multimodal inputs. The problem sidesteps the clinical task of early ADHD detection and adds prediction of sex as a meta-feature to enhance robustness. The architecture is applied to demographic profiles, quantitative tests, and functional brain connectomes as 200 × 200 matrices. Preprocessing includes data harmonization, matrix symmetrization, graph-based descriptor extraction, including total strength, mean, and standard deviation, categorical encoding, variance thresholding, and imputation of missing values using k-nearest neighbors. Sex classification is performed using XGBoost with stratified cross-validation to generate probability outputs that enhance the ADHD model. ADHD classification is tuned using FLAML’s automatic hyperparameter search for XGBoost and class-weighting to address imbalance. Findings show that combining imaging-derived features and automated model selection yields a robust method of ADHD detection, underscoring the utility of multimodal data fusion in neuropsychiatric studies.