Lin IC, Chang SC, Huang YJ, Kuo TBJ, Chiu HW. Distinguishing different types of attention deficit hyperactivity disorder in children using artificial neural network with clinical intelligent test.
Front Psychol 2023;
13:1067771. [PMID:
36710799 PMCID:
PMC9875079 DOI:
10.3389/fpsyg.2022.1067771]
[Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2022] [Accepted: 12/22/2022] [Indexed: 01/12/2023] Open
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.
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