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

Abstract

BackgroundAttention 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.PurposeTo construct models that automatically distinguish between children with predominantly inattentive-type ADHD (ADHD-I), with combined-type ADHD (ADHD-C), and without ADHD.MethodsClinical 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.ResultsWe 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.ConclusionThis 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.

A systematic review on the application of machine learning models in psychometric questionnaires for the diagnosis of attention deficit hyperactivity disorder

“…Table 1 summarizes the characteristics of the articles that refer to the use of ML on psychometric questionnaires for the diagnosis of ADHD. Of the 17 articles reviewed eight used random forest (RF) (Cordova et al, 2020; Davakumar & Siromoney, 2020; Goh et al, 2023; Grazioli et al, 2023; Haque et al, 2023; Kim et al, 2021, 2023; Tachmazidis et al, 2020), seven decision tree (DT) (Ardulov et al, 2021; Bledsoe et al, 2020; Chen et al, 2023; Christiansen et al, 2020; Grazioli et al, 2023; Haque et al, 2023; Tachmazidis et al, 2020), six support vector machine (SVM) (Bledsoe et al, 2020; Chen et al, 2023; Davakumar & Siromoney, 2020; Duda et al, 2016; Grazioli et al, 2023; Tachmazidis et al, 2020), four linear discriminant analysis (LDA) (Chen et al, 2023; Duda et al, 2016, 2017; Kim et al, 2021), three k‐nearest neighbours (KNN) (Chen et al, 2023; Kim et al, 2021; Tachmazidis et al, 2020), three Gaussian Naïve Bayes (Chen et al, 2023; Haque et al, 2023; Tachmazidis et al, 2020), three logistic regression (LR) (Chen et al, 2023; Duda et al, 2016; Tachmazidis et al, 2020), three artificial neural network (ANN) (Chen et al, 2023; Davakumar & Siromoney, 2020; Lin et al, 2023), two Lasso regression (Duda et al, 2016; Weigard et al, 2023), two gradient boosting (GB) (Chen et al, 2023; Kim et al, 2023), two elastic net (ENet) (Duda et al, 2017; Liu et al, 2023), one Q‐learning (Ardulov et al, 2021) and one principal components regression (PCR) (Weigard et al, 2023).…”

“…On the other hand, the questionnaires used in the different studies on which ML techniques were applied varied widely. Among them, 13 studies used scales that diagnosed ADHD (Bledsoe et al, 2020; Chen et al, 2023; Christiansen et al, 2020; Cordova et al, 2020; Davakumar & Siromoney, 2020; Goh et al, 2023; Grazioli et al, 2023; Haque et al, 2023; Kim et al, 2023; Lin et al, 2023; Liu et al, 2023; Tachmazidis et al, 2020; Weigard et al, 2023), four articles used questionnaires assessing social skills (Duda et al, 2016, 2017; Goh et al, 2023; Kim et al, 2021), an article administered a test diagnosing ASD (Ardulov et al, 2021), a study used a questionnaire that assessed personality (Kim et al, 2021), an article used a scale that measured intelligence (Grazioli et al, 2023), and an article used a test that assessed academic performance (Goh et al, 2023).…”

“…In terms of measures for assessing ML models, not all articles specify these. A group of 11 out of 17 studies specified the accuracy of the techniques used (Bledsoe et al, 2020; Christiansen et al, 2020; Davakumar & Siromoney, 2020; Goh et al, 2023; Grazioli et al, 2023; S. Kim et al, 2021; Lin et al, 2023; Tachmazidis et al, 2020). The accuracy of ML techniques varies from study to study.…”

“…The accuracy of ML techniques varies from study to study. In particular, one study reports an accuracy of 35% to 70% (Davakumar & Siromoney, 2020), two articles from 70% to 80% (Chen et al, 2023; Lin et al, 2023) and eight items greater than 80% (Bledsoe et al, 2020; Chen et al, 2023; Christiansen et al, 2020; Duda et al, 2017; Goh et al, 2023; Haque et al, 2023; Kim et al, 2021; Tachmazidis et al, 2020). However, to determine the representativeness of the above values, it is necessary to compare them against the base rates, that is, with respect to the percentage of observations of the majority class, which can be found in Table 1.…”

“…Some of the health disorders taken into account in these studies are dyslexia (Davakumar & Siromoney, 2020), autism spectrum disorder (ASD) (Ardulov et al, 2021;Cordova et al, 2020;Duda et al, 2016Duda et al, , 2017, obesity (Christiansen et al, 2020), or problematic gambling (Christiansen et al, 2020). It should be noted that only one study attempts to differentiate between inattentive and combined ADHD subtypes (Lin et al, 2023).…”