Distinguishing different types of attention deficit hyperactivity disorder in children using artificial neural network with clinical intelligent test - Lin, I-Cheng; Chang, Shen-Chieh; Huang, Yu-Jui; Kuo, Terry B. J.; Chiu, Hung-Wen (Frontiers in Psychology, 2023)
Background: Attention deficit hyperactivity disorder (ADHD) is a well-studied topic in child and adolescent psychiatry. ADHD diagnosis relies on information from an assessment scale used by teachers and parents and psychological assessment by physicians; however, the assessment results can be inconsistent. Purpose: To construct models that automatically distinguish between children with predominantly inattentive-type ADHD (ADHD-I), with combined-type ADHD (ADHD-C), and without ADHD. Methods: Clinical records with age 6 to 17 years-old, for January 2011–September 2020 were collected from local general hospitals in northern Taiwan; the data were based on the SNAP-IV scale, the second and third editions of Conners' Continuous Performance Test (CPT), and various intelligence tests. This study used an artificial neural network to construct the models. In addition, k-fold cross-validation was applied to ensure the consistency of the machine learning results. Results: We collected 328 records using CPT-3 and 239 records using CPT-2. With regard to distinguishing between ADHD-I and ADHD-C, a combination of demographic information, SNAP-IV scale results, and CPT-2 results yielded overall accuracies of 88.75% and 85.56% in the training and testing sets, respectively. The replacement of CPT-2 with CPT-3 results in this model yielded an overall accuracy of 90.46% in the training set and 89.44% in the testing set. With regard to distinguishing between ADHD-I, ADHD-C, and the absence of ADHD, a combination of demographic information, SNAP-IV scale results, and CPT-2 results yielded overall accuracies of 86.74% and 77.43% in the training and testing sets, respectively. Conclusions: This proposed model distinguished between the ADHD-I and ADHD-C groups with 85%–90% accuracy, and it distinguished between the ADHD-I, ADHD-C, and control groups with 77%–86% accuracy. The machine learning model helps clinicians identify patients with ADHD in a timely manner.