Optimal interval and feature selection in activity data for detecting attention deficit hyperactivity disorder

• Published in: Computers in biology and medicine Vol. 179; p. 108909
• Main Authors: V, Shafna, S D, Madhu Kumar
• Format: Journal Article
• Language: English
• Published: United States Elsevier Ltd 01.09.2024; Elsevier Limited
• Subjects: Accuracy; Activity data; Artificial intelligence; Attention deficit hyperactivity disorder; Decision trees; Deep learning; Diagnosis; Feature selection; Hyperactivity; Impulsive behavior; Learning algorithms; Machine learning; Patients; Performance prediction; Predictive control; Principal component analysis; Deep learning; Feature selection; Activity data; Attention deficit hyperactivity disorder; Machine learning; Principal component analysis
• ISSN: 0010-4825; 1879-0534; 1879-0534
• DOI: 10.1016/j.compbiomed.2024.108909

Attention deficit hyperactivity disorder (ADHD) is a heterogeneous neurobehavioral disorder that is common in children and adolescents. Inattention, impulsivity, and hyperactivity are the key symptoms of ADHD patients. Traditional clinical assessments delay ADHD diagnosis and increase undiagnosed cases and costs, as well. The use of deep learning (DL) and machine learning (ML)-based objective techniques for diagnosing ADHD has grown exponentially in recent years as the efficiency of early diagnosis has improved. This research highlights the significance of utilizing feature selection techniques before constructing machine learning models on activity datasets. It also explores the distinctions between specific time-interval activity data and broader interval activity data in identifying ADHD patients from the clinical control group. Five ML models were developed and tested to assess the performance of two sets of features and different categories of activity data in predicting ADHD. The study concludes with the following findings: (i) the model’s performance showed a notable improvement of 0.11 in accuracy with the adoption of a precise feature selection process; (ii) activity data recorded in the morning and at night are more significant predictors of ADHD compared to other times; (iii) the utilization of specific time interval data is crucial for ADHD prediction; (iv) the random forest performs better than the other machine learning models used in the study, with 84% accuracy, 79% precision, 85% F1-score, and 92% recall. As we move into an era where early disease prediction is possible, combining artificial intelligence methods with activity data could create a strong framework for helping doctors make decisions that can be used far beyond hospitals. •Actigraphy-based ADHD diagnosis offers a cost-effective, non-invasive alternative to MRI and EEG.•Specific time-interval activity data is more optimal for building predictive ADHD models.•An 11% accuracy gain highlights the critical importance of optimal feature selection.•Morning and night activity data are key predictors for ADHD over noon and evening.