Neuro-statistical analysis for ADHD detection

Attention-deficit/hyperactivity disorder (ADHD) is a complex neurodevelopmental disorder characterized by symptoms such as inattention, hyperactivity, and impulsivity. Early and accurate diagnosis of ADHD is crucial for effective intervention and management. This project presents a novel approach to detect ADHD in individuals using brain scan data and machine learning techniques. The study utilizes a brain scan-making machine, and the data is pre-processed and analysed using popular Python libraries, including NumPy and Pandas. To develop the predictive model, TensorFlow and TensorFlow Keras layers are employed for building and training the deep learning model. The proposed methodology aims to identify distinct brain activity patterns associated with ADHD, allowing for the reliable prediction of ADHD presence in individuals. The project emphasizes the potential of machine learning in neuroimaging research for ADHD detection, with practical implications for early diagnosis and personalized treatment strategies.