Machine Learning-Based Automated Detection of ADHD Using Heart Rate Variability Data

This study addresses the pressing need for effective methods in detecting Attention-Deficit/Hyperactivity Disorder (ADHD), a neurodevelopmental condition significantly impacting individuals' attention, impulse control, and activity regulation. Leveraging advancements in machine learning and wearable technology, the research explores the potential of Heart Rate Variability (HRV) data as a novel source for ADHD detection. Six machine learning algorithms, including Logistic Regression, Random Forest, XGBoost, LightGBM, Neural Network, and Support Vector Machine, were rigorously investigated using an HRV dataset, marking a pioneering effort in utilizing HRV data for ADHD identification. The results demonstrate promising performance, with Logistic Regression exhibiting the highest F1 score (0.71), and Support Vector Machine achieving the highest Matthews Correlation Coefficient (0.44). This study showcases the capacity of machine learning utilizing HRV data for identifying ADHD, contributing to the evolving landscape of machine learning applications in mental health diagnostics.