VISUAL AND BEHAVIOURAL DATA-DRIVEN ADHD CLASSIFICATION USING SUPERVISED LEARNING

The Attention-Deficit/Hyperactivity Disorder (ADHD) is a neurodevelopmental disorder affecting children worldwide, characterized by inattention, hyperactivity, and impulsivity. In India, ADHD prevalence ranges between 5% and 15% among school-aged children. Despite increasing awareness, early diagnosis remains a challenge due to social stigma and a lack of standardized screening methods. Traditional diagnosis relies heavily on clinical observation and questionnaires, leading to potential biases and inconsistencies. This study introduces a novel ADHD detection system that integrates a user-friendly graphical user interface (GUI) with advanced machine learning models, particularly emphasizing a Logistic Regression Classifier (LRC) to achieve superior performance. The methodology innovates by combining robust data preprocessing (shuffling and normalization) with a balanced 80:20 train-test split of a 496-record dataset containing 36 movement features extracted from behavioral data. Unlike traditional methods, the system processes both image and video inputs, enabling dynamic and real-world applicable ADHD classification.. By leveraging LRC’s ability to model complex relationships in movement data, the system outperforms Naive Bayes (NBC) and Support Vector Machine (SVM), addressing limitations in feature independence assumptions and computational complexity. This approach enhances diagnostic consistency and supports early intervention by providing a scalable, accurate, and practical tool for ADHD detection. The proposed system demonstrates exceptional performance, with the LRC achieving a 97.0% accuracy, 97.0713073% precision, 96.6162253% recall, and 96.8324359% F-score, surpassing NBC (91.0% accuracy) and SVM (94.0% accuracy) on the test set of 100 records. The system’s ability to predict ADHD from image and video inputs via the GUI highlights its practical utility in clinical settings, offering a reliable, automated alternative to subjective assessments