Decoding attention: predictive modeling of ADHD using multi-modal data and ML

Attention-deficit/hyperactivity disorder (ADHD) is a well-known and growing neurodevelopmental disorder that causes symptoms like inattention, restlessness, hyperactivity, and impulsiveness, which can make it hard to get along with others. Timely intervention can greatly improve outcomes for people with ADHD, especially when it is possible to predict them early on. This study looks at how to use machine learning (ML) algorithms like Logistic Regression (LR), Light Gradient Boosting Machine (LightGBM), and CatBoost to predict ADHD based on behavioral and clinical data. To fix class imbalance, make the model more transparent, improve accuracy, and make sure it can be understood, we use a full range of data preprocessing and feature selection methods. We use LR as a baseline model because it is easy to understand and use. We use LightGBM and CatBoost to find complicated, non-linear relationships in the data. How well the model works is assessed using metrics like AUC-ROC, F I -score, recall, accuracy, and precision. According to the results, CatBoost and other gradient boosting models perform better than LR in terms of accuracy and stability, suggesting that they could be useful in clinical decision support systems for diagnosing ADHD.