Attention-Deficit Hyperactivity Disorder Prediction by Artificial Intelligence Techniques

Rasha H.  Ali; Wisal Hashim  Abdulsalam

doi:10.24996/ijs.2024.65.9.39

Authors

Rasha H. Ali Computer Science Department, College of Education for Women, University of Baghdad, Baghdad, Iraq https://orcid.org/0000-0003-3644-5979
Wisal Hashim Abdulsalam Computer Science Department, College of Education for Pure Science/Ibn-Al-Haitham, University of Baghdad, Baghdad, Iraq https://orcid.org/0000-0003-0453-095X

DOI:

https://doi.org/10.24996/ijs.2024.65.9.39

Keywords:

Prediction, Attention Deficit Hyperactivity Disorder (ADHD), Artificial Intelligence, KNN, AdaBoost, XGBoost, Pearson correlation

Abstract

Attention-Deficit Hyperactivity Disorder (ADHD), a neurodevelopmental disorder affecting millions of people globally, is defined by symptoms of hyperactivity, impulsivity, and inattention that can significantly affect an individual's daily life. The diagnostic process for ADHD is complex, requiring a combination of clinical assessments and subjective evaluations. However, recent advances in artificial intelligence (AI) techniques have shown promise in predicting ADHD and providing an early diagnosis. In this study, we will explore the application of two AI techniques, K-Nearest Neighbors (KNN) and Adaptive Boosting (AdaBoost), in predicting ADHD using the Python programming language. The classification accuracies obtained were 96.5% and 93.47%, respectively, before applying balancing to the data. In addition, 98.59% and 97.18%, respectively, after applying the balancing technique The extreme gradient boosting (XGBoost) technique had been applied to selecting the important features and the Pearson correlation for finding the correlation between features.