Distinguishing different types of attention deficit hyperactivity disorder in children using artificial neural network with clinical intelligent test

I-Cheng Lin; Shen-Chieh Chang; Yu-Jui Huang; Terry B J Kuo; Hung-Wen Chiu

doi:10.3389/fpsyg.2022.1067771

Distinguishing different types of attention deficit hyperactivity disorder in children using artificial neural network with clinical intelligent test

Front Psychol. 2023 Jan 11:13:1067771. doi: 10.3389/fpsyg.2022.1067771. eCollection 2022.

Authors

I-Cheng Lin^{1

2}, Shen-Chieh Chang³, Yu-Jui Huang⁴, Terry B J Kuo⁵, Hung-Wen Chiu^{1

6

7}

Affiliations

¹ Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan.
² Department of Psychiatry, Shuang Ho Hospital, Taipei Medical University, New Taipei City, Taiwan.
³ Department of Psychiatry, Taipei Municipal Wan Fang Hospital, Taipei Medical University, Taipei, Taiwan.
⁴ Department of Psychiatry and Psychiatric Research Center, Taipei Medical University Hospital, Taipei, Taiwan.
⁵ Institute of Brain Science, National Yang Ming Chiao Tung University, Taipei, Taiwan.
⁶ Clinical Big Data Research Center, Taipei Medical University Hospital, Taipei, Taiwan.
⁷ Bioinformatics Data Science Center, Wan Fang Hospital, Taipei Medical University, Taipei, Taiwan.

Abstract

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 6-17 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.

Conclusion: 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.

Keywords: artificial intelligence; attention deficit; hyperactivity; machine learning; neural network.