EVALUATION OF ADHD: CLASSIFICATION, TREATMENT STRATEGIES IN PEDIATRIC AND ADOLESCENT POPULATIONS USING MACHINE LEARNING

Attention-Deficit/Hyperactivity Disorder (ADHD) is a neurodevelopmental disorder characterized by inattention, impulsivity, and hyperactivity. Despite extensive research, existing diagnostic approaches rely heavily on subjective clinical assessments, leading to misdiagnoses and delayed interventions. Furthermore, conventional treatment strategies lack personalization, often resulting in suboptimal therapeutic outcomes. This study addresses these gaps by leveraging machine learning techniques to enhance ADHD classification and treatment strategies, specifically for pediatric and adolescent populations. Unlike previous studies that primarily focus on symptom-based categorization, our research integrates multi-modal data, including neuroimaging, behavioral assessments, and genetic markers, to improve diagnostic accuracy. A novel hybrid machine learning model is proposed, incorporating convolutional autoencoders and advanced neural architectures to extract discriminative features for precise classification. Additionally, the study explores AI-driven personalized treatment recommendations, optimizing intervention strategies based on patient-specific patterns. The findings of this research contribute to the development of an objective, data-driven framework for ADHD diagnosis and treatment, reducing reliance on subjective evaluation. This work establishes a foundation for future AI-assisted clinical decision-making, ultimately improving patient outcomes and advancing ADHD research.