Use of machine learning to classify adult ADHD and other conditions based on the Conners’ Adult ADHD Rating Scales

Christiansen, Hanna; Chavanon, Mira‐Lynn; Hirsch, Oliver; Schmidt, Martin H.; Meyer, Christian; Müller, Astrid; Rumpf, Hans‐Jürgen; Grigorev, Ilya; Hoffmann, Ary A.

doi:10.1038/s41598-020-75868-y

Cited by 29 publications

(28 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Since the incidence rate of ADHD is high, it is necessary to provide a tool that can swiftly and correctly predict the risk of ADHD. There were some ML-based works in previous studies to correctly detect and predict ADHD [28,29,[64][65][66] and children with ADHD who received treatment [32]. Our current study expands these previous works by implementing an LR-based model for the risk factor extraction method and eight ML-based classifiers for the prediction of the children with ADHD.…”

Section: Discussionmentioning

confidence: 82%

Predicting Children with ADHD Using Behavioral Activity: A Machine Learning Analysis

2022

View full text Add to dashboard Cite

Attention deficit hyperactivity disorder (ADHD) is one of childhood’s most frequent neurobehavioral disorders. The purpose of this study is to: (i) extract the most prominent risk factors for children with ADHD; and (ii) propose a machine learning (ML)-based approach to classify children as either having ADHD or healthy. We extracted the data of 45,779 children aged 3–17 years from the 2018–2019 National Survey of Children’s Health (NSCH, 2018–2019). About 5218 (11.4%) of children were ADHD, and the rest of the children were healthy. Since the class label is highly imbalanced, we adopted a combination of oversampling and undersampling approaches to make a balanced class label. We adopted logistic regression (LR) to extract the significant factors for children with ADHD based on p-values (<0.05). Eight ML-based classifiers such as random forest (RF), Naïve Bayes (NB), decision tree (DT), XGBoost, k-nearest neighborhood (KNN), multilayer perceptron (MLP), support vector machine (SVM), and 1-dimensional convolution neural network (1D CNN) were adopted for the prediction of children with ADHD. The average age of the children with ADHD was 12.4 ± 3.4 years. Our findings showed that RF-based classifier provided the highest classification accuracy of 85.5%, sensitivity of 84.4%, specificity of 86.4%, and an AUC of 0.94. This study illustrated that LR with RF-based system could provide excellent accuracy for classifying and predicting children with ADHD. This system will be helpful for early detection and diagnosis of ADHD.

show abstract

Section: Discussionmentioning

confidence: 82%

Predicting Children with ADHD Using Behavioral Activity: A Machine Learning Analysis

2022

View full text Add to dashboard Cite

show abstract

“…The prediction of schizophrenia has been successful using linguistic features such as semantic relatedness of individuals with schizophrenia or those at a high risk to develop acute symptoms [42, 61-64]. Looking at the studies that have investigated the machine learning based prediction of ADHD from other biological signals points towards a similar performance of approaches based on neuropsychological performance [65], EEG-measures [55, 66], questionnaires [67] or resting state fMRI [68-70] as compared to our findings. In summary, the findings in this study are broadly comparable to previous research using voice to predict mental disorders or other biological signals to predict ADHD.…”

Section: Discussionmentioning

confidence: 99%

Predicting adult Attention Deficit Hyperactivity Disorder (ADHD) using vocal acoustic features

Polier

Ahlers

Amunts

et al. 2021

Preprint

View full text Add to dashboard Cite

Background: It is a key concern in psychiatric research to investigate objective measures to support and ultimately improve diagnostic processes. Current gold standard diagnostic procedures for attention deficit hyperactivity disorder (ADHD) are mainly subjective and prone to bias. Objective measures such as neuropsychological measures and EEG markers show limited specificity. Recent studies point to alterations of voice and speech production to reflect psychiatric symptoms also related to ADHD. However, studies investigating voice in large clinical samples allowing for individual-level prediction of ADHD are lacking. The aim of this study was to explore a role of prosodic voice measures as objective marker of ADHD. Methods: 1005 recordings were analyzed from 387 ADHD patients, 204 healthy controls, and 100 clinical (psychiatric) controls. All participants (age range 18-59 years, mean age 34.4) underwent an extensive diagnostic examination according to gold standard methods and provided speech samples (3 min in total) including free and given speech. Paralinguistic features were calculated, and random forest based classifications were performed using a 10-fold cross-validation with 100 repetitions controlling for age, sex, and education. Association of voice features and ADHD-symptom severity assessed in the clinical interview were analyzed using random forest regressions. Results and Conclusion: ADHD was predicted with AUC = 0.76. The analysis of a non-comorbid sample of ADHD resulted in similar classification performance. Paralinguistic features were associated with ADHD-symptom severity as indicated by random forest regression. In female participants, particularly with age < 32 years, paralinguistic features showed the highest classification performance (AUC = 0.86). Paralinguistic features based on derivatives of loudness and fundamental frequency seem to be promising candidates for further research into vocal acoustic biomarkers of ADHD. Given the relatively good performance in female participants independent of comorbidity, vocal measures may evolve as a clinically supportive option in the complex diagnostic process in this patient group.

show abstract

“…In recent years, machine learning has made remarkable progress, from its application in detecting between-group differences to making predictions on the individual level 4 . Concerning ADHD, previous studies based on clinical and/or neuroimaging data have performed automated classi cations to distinguish between ADHD and typically developing individuals with classi cation accuracies ranging from 62-89.5% [5][6][7][8][9] . Unfortunately, this dichotomous distinction between the labels of "typically developing" and "ADHD" does not re ect the question typically asked in the clinical setting.…”

Section: Introductionmentioning

confidence: 99%

Training a Machine Learning Classifier to Identify ADHD Based on Real-World Clinical Data From Medical Records

Mikoláš

Vahid

Bernardoni

et al. 2022

Preprint

View full text Add to dashboard Cite

The diagnostic process of attention deficit hyperactivity disorder (ADHD) is complex and relies on criteria sensitive to subjective biases. This may cause significant delays in appropriate treatment initiation. An automated analysis relying on subjective and objective measures might not only simplify the diagnostic process and reduce the time to diagnosis, but also improve reproducibility. While recent machine learning studies have succeeded at distinguishing ADHD from healthy controls, the clinical process requires differentiating among other or multiple psychiatric conditions. We trained a linear support vector machine (SVM) classifier to detect participants with ADHD in a population showing a broad spectrum of psychiatric conditions using anonymized data from clinical records (N = 299 participants). We differentiated children and adolescents with ADHD from those not having the condition with an accuracy of 66.1 %. SVM using single features showed slight differences between single features and overlapping standard deviations of the achieved accuracies. An automated feature selection achieved the best performance using a combination 19 features. Real-life clinical data from medical records can be used to automatically identify individuals with ADHD among help-seeking individuals using machine learning. The relevant diagnostic information can be reduced using an automated feature selection without loss of performance. A broad combination of symptoms across different domains, rather than specific domains, seems to indicate an ADHD diagnosis.

show abstract

Use of machine learning to classify adult ADHD and other conditions based on the Conners’ Adult ADHD Rating Scales

Cited by 29 publications

References 36 publications

Predicting Children with ADHD Using Behavioral Activity: A Machine Learning Analysis

Predicting Children with ADHD Using Behavioral Activity: A Machine Learning Analysis

Predicting adult Attention Deficit Hyperactivity Disorder (ADHD) using vocal acoustic features

Training a Machine Learning Classifier to Identify ADHD Based on Real-World Clinical Data From Medical Records

Contact Info

Product

Resources

About