A Predictive Model Offor Attention Deficit Hyperactivity Disorder Based on Clinical Assessment Tools

Dai Han; Yantong Fang; Hong Luo

doi:10.2147/NDT.S245636

A Predictive Model Offor Attention Deficit Hyperactivity Disorder Based on Clinical Assessment Tools

Neuropsychiatr Dis Treat. 2020 May 25:16:1331-1337. doi: 10.2147/NDT.S245636. eCollection 2020.

Authors

Dai Han^{1

2

3}, Yantong Fang², Hong Luo^{1

2}

Affiliations

¹ Institutes of Psychological Sciences, Hangzhou Normal University, Hangzhou, Zhejiang, People's Republic of China.
² Children and Adolescents Mental Health Joint Clinic, The Affiliated Hospital of Hangzhou Normal University, Hangzhou, Zhejiang, People's Republic of China.
³ Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou, Zhejiang, People's Republic of China.

Abstract

Background: At present, clinicians diagnose that the clinical diagnosis of attention deficit hyperactivity disorder (ADHD) in children is mainly on the basis of the information provided by their parents, the behaviour of children in clinical clinics and the assessments of clinical rating scales and neuropsychological tests. Notably, no unified standard exists currently for analysing the results of various measurement tools for diagnosing ADHD. Therefore, clinicians interpret the results of clinical rating scales and neuropsychological tests entirely based on their clinical experience.

Methods and subjects: To provide guidance for clinicians on how to analyse the results of various clinical assessment tools when diagnosing ADHD, this study assessed children with ADHD and children in the control group using two clinical assessment scales-parent rating scale (PSQ) and Child Behavior Checklist (CBCL)-and one neuropsychological test (Integrated Visual and Auditory Continuous Performance Testing). The two-sample t-test (FDR correction) screened the parameters of the three assessment tools with significant inter-group differences. LibSVM was used to establish a classification prediction model for analysing the accuracy of ADHD prediction using parameters of the three assessment tools and weight values of each parameter for classification prediction.

Results: A total of 19 parameters (16 from clinical rating scales, 3 from neuropsychological tests) with significant inter-group differences were screened. The accuracy of classification modelling was higher for the clinical rating scales (61.635%) than for the neuropsychological test (59.784%), whereas the accuracy of classification modelling was higher for the clinical rating scales combined with the neuropsychological test (70.440%) than for the former two parameters alone. The three parameters with the highest weight values were learning problem (0.731), hyperactivity/impulsivity (0.676) and activity capacity (0.569). The three parameters with the lowest weight values are integrated control force (0.028), visual attention (0.028) and integrated attention (0.034).

Conclusion: Our study findings indicate that the diagnosis of ADHD should be based on multidimensional assessment. For a more accurate diagnosis of ADHD, assessments and that more assessment parameters should be developed on the basis of different dimensions of physiology or psychology in the future to obtain a more accurate diagnosis of ADHD. Furthermore, the predictive model for ADHD may improve our understanding and help in optimisation of the treatment of such a condition.

Keywords: LibSVM; attention deficit hyperactivity disorder; child behavior checklist; integrated visual and auditory continuous performance testing; parent rating scale.