Diagnosing attention-deficit hyperactivity disorder (ADHD) using artificial intelligence: a clinical study in the UK

Abstract

Attention-deficit hyperactivity disorder (ADHD) is a neurodevelopmental disorder affecting a large percentage of the adult population. A series of ongoing efforts has led to the development of a hybrid AI algorithm (a combination of a machine learning model and a knowledge-based model) for assisting adult ADHD diagnosis, and its clinical trial currently operating in the largest National Health Service (NHS) for adults with ADHD in the UK. Most recently, more data was made available that has lead to a total collection of 501 anonymized records as of 2022 July. This prompted the ongoing research to carefully examine the model by retraining and optimizing the machine learning algorithm in order to update the model with better generalization capability. Based on the large data collection so far, this paper also pilots a study to examine the effectiveness of variables other than the Diagnostic Interview for ADHD in adults (DIVA) assessment, which adds considerable cost in the screenining process as it relies on specially trained senior clinicians. Results reported in this paper demonstrate that the newly trained machine learning model reaches an accuracy of 75.03% when all features are used; the hybrid model obtains an accuracy of 93.61%. Exceeding what clinical experts expected in the absence of DIVA, achieving an accuracy of 65.27% using a rule-based machine learning model alone encourages the development of a cost effective model in the future.

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, 2021Kim et al, , 2023Tachmazidis 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, 2016Duda et al, , 2017Kim 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;...…”

“…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(Duda et al, , 2017Goh 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).…”

Artificial Intelligence and Machine Learning Based Approach to Motivate and Assist Primary School ADHD Children

“…The objective is to assess improvements in ADHD symptoms, including attention, impulsivity, and hyperactivity levels among the participating children. [11] Ethical considerations related to the use of AI and ML interventions for primary school children with ADHD will be addressed. This includes identifying and addressing privacy concerns, data security, and potential unintended consequences.…”

Constructing Features for Screening Neurodevelopmental Disorders Using Grammatical Evolution

“…Autism Spectrum enhance accuracy, and also facilitate comprehension of the techniques and algorithms employed for various types of data. Multiple studies have been conducted on ASD [22][23][24][25][26][27][28], ADHD [29][30][31], ID [9,10,32], SLD [33,34], CD [35], and NDs [4,19,20,31,[36][37][38], providing evidence that ML algorithms can enhance diagnostic strategies for NDs. Further, more research efforts that seek to investigate ML approaches for early detection and diagnosis of NDs in real-life situations are crucial for ensuring timely intervention and optimizing lifelong outcomes [17,19,20,[39][40][41].…”