Automated ADHD detection using dual-modal sensory data and machine learning

Abstract

This study explores using dual-modal sensory data and machine learning to objectively identify Attention-Deficit/Hyperactivity Disorder (ADHD), a neurodevelopmental disorder traditionally diagnosed through subjective clinical evaluations. Six machine learning algorithms, including Logistic Regression (LR), Random Forest (RF), XGBoost (XGB), LightGBM (LGBM), Neural Network (NN), and Support Vector Machine (SVM), were evaluated using both activity and heart rate variability (HRV) data collected from 103 participants. The results show that both activity and HRV data performed similarly when analyzed individually. However, when the two datasets were combined, the highest F1-score increased by 12 % compared to the activity data and 23 % compared to the HRV data. This combination leverages the complementary strengths of both data, representing a key contribution of our work. With the combined data, the SVM model performed best, achieving an F1-Score of 0.87 and a Matthews Correlation Coefficient of 0.77. This study highlights the significant potential of interdisciplinary collaboration and the use of diverse data sources to advance ADHD detection through cutting-edge machine learning techniques.