ADHD Prediction in Children Through Machine Learning Algorithms

Abstract

Attention-deficit/hyperactivity disorder (ADHD) is a neurodevelopmental disorder that affects approximately 5% of children worldwide. It is typically diagnosed based on the presence of inattentive and hyperactive symptoms. Our objective is to identify ADHD from a Machine Learning (ML) perspective, utilizing symptom information and features such as socioeconomic status, social behavior, academic competence, and quality of life. We conducted extensive experiments using the CAP dataset and various machine learning algorithms, including logistic regression, k-nearest neighbors, Support Vector Machines (SVMs), Random Forest, XGBoost, and an Artificial Neural Network (ANN). The ANN model demonstrated the highest accuracy, achieving an AUC metric of 0.99. As a result, we conclude that using ML algorithms to predict ADHD provides a better understanding of the etiological factors associated with the disorder and has the potential to form the basis for a more precise diagnostic approach. The code is available at: GitHub Repository.