Smart Health Monitoring Approach to Diagnose Attention-Deficit Hyperactivity Disorder based on Real-Time Activity and Heart Rate Variability using Boosting Models

Introduction: Attention-Deficit Hyperactivity Disorder (ADHD) is a prevalent chronic mental health condition that significantly impacts the psychological and physical wellbeing of millions of adolescents. Early detection and accurate diagnosis are crucial for effective treatment and mitigating the disorder's adverse effects. Despite extensive research efforts, current methods often fall short in simultaneously accounting for daily motor activity and heart rate variability in ADHD detection. Methods: Addressing these gaps, this paper introduces a histogram-based gradient-boosting classifier for analyzing real-time activity and heart-rate variability data to automate ADHD diagnosis. By extracting twelve key features from the data and selecting the most significant ones with the extra tree model, we evaluate these features using various classifiers, including histogram-based gradient boosting, light gradient boosting machine, extreme gradient boosting, gradient boosting, and adaptive boosting. Results: The histogram-based gradient-boosting model, validated through ten-fold crossvalidation, outperforms other classifiers with an accuracy of 99.12%, an F1 measure of 99.12%, and a sensitivity of 99.13%. Additionally, it achieves a specificity of 99.1%, an AUC of 0.9995, and a minimal FDR of 0.88%. These results demonstrate that the proposed approach offers a highly effective and precise solution for automated ADHD diagnosis. Conclusion: The implications of these findings suggest that integrating real-time activity and heart-rate variability data into diagnostic processes can significantly enhance the accuracy and efficiency of ADHD assessment, potentially leading to earlier and more reliable diagnoses, improved patient outcomes, and more tailored treatment strategies.