Authors - Shoh-Jakhon Khamdаmov, Muazzam Akramova, Rano Abdullaevna Sadikova, Azamat Kasimov, Jasurbek Pozilovich Kurbonov, Alisher Bakberganovich Sherov, Dilshoda Akramova Abstract - Attention Deficit Hyperactivity Disorder (ADHD) is one of the most common neurodevelopmental disorders in children, characterized by inattention, hyperactivity, and impulsivity that impair academic and social functioning. Due to its heterogeneous presentation and symptom overlap with other cognitive disorders, early and accurate diagnosis remains challenging. This study proposes a multimodal machine learning framework integrating behavioral, neuroimaging, and physiological data to predict ADHD in children. Convolutional Neural Networks (CNNs) are used to extract features from brain MRI scans, Long Short-Term Memory (LSTM) networks model temporal patterns in physiological signals such as EEG and heart rate variability, and ensemble learning methods incorporate behavioral and clinical attributes. Both feature-level and decision-level fusion strategies are evaluated. Results on benchmark datasets show that the multimodal model consistently outperforms unimodal approaches in accuracy, sensitivity, and F1- score, demonstrating the potential of AI-driven multimodal systems for early, objective, and interpretable ADHD diagnosis.
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