PANDAS: Paediatric Attention-Deficit/Hyperactivity Disorder Application Software
| Autor: | Dawie van den Heever, Pieter R. Fourie, Hervé Mukenya Mwamba |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
050103 clinical psychology
Support Vector Machine Application software computer.software_genre lcsh:Technology lcsh:Chemistry 0302 clinical medicine PANDAS Prevalence General Materials Science Child Instrumentation lcsh:QH301-705.5 Fluid Flow and Transfer Processes 05 social sciences General Engineering lcsh:QC1-999 Computer Science Applications Neuropsychiatric disorder machine learning Child Preschool Psychology Clinical psychology Adolescent SVM Feature extraction MEDLINE 03 medical and health sciences children Classifier (linguistics) medicine Attention deficit hyperactivity disorder ADHD Humans 0501 psychology and cognitive sciences novel lcsh:T Process Chemistry and Technology screening medicine.disease Support vector machine lcsh:Biology (General) lcsh:QD1-999 Sample size determination lcsh:TA1-2040 Attention Deficit Disorder with Hyperactivity Test set Sample Size lcsh:Engineering (General). Civil engineering (General) computer 030217 neurology & neurosurgery lcsh:Physics Software |
| Zdroj: | Applied Sciences Volume 9 Issue 8 Applied Sciences, Vol 9, Iss 8, p 1645 (2019) EMBC |
| ISSN: | 2694-0604 |
| Popis: | Attention-deficit/hyperactivity disorder (ADHD) is a common neuropsychiatric disorder that impairs social, academic and occupational functioning in children, adolescents and adults. In South Africa, youth prevalence of ADHD is estimated as 10%. It is therefore necessary to further investigate methods that objectively diagnose, treat and manage the disorder. The aim of the study was to develop a novel method that could be used as an aid to provide screening for ADHD. The study comprised of a beta-testing phase that included 30 children (19 non-ADHD and 11 ADHD) between the ages of 5 and 16 years old. The strategy was to use a tablet-based game that gathered real-time user data during game-play. This data was then used to train a linear binary support vector machine (SVM). The objective of the SVM was to differentiate between an ADHD individual versus a non-ADHD individual. A feature set was extracted from the gathered data and sequential forward selection (SFS) was performed to select the most significant features. The test set accuracy of 85.7% and leave-one-out cross-validation (LOOCV) accuracy of 83.5% were achieved. Overall, the classification accuracy of the trained SVM was 86.5%. Finally, the sensitivity of the model was 75% and this was seen as a moderate result. Since the sample size was fairly small, the results of the classifier were only seen as suggestive rather than conclusive. Therefore, the performance of the classifier was indicative that a quantitative tool could indeed be developed to perform screening for ADHD. |
| Databáze: | OpenAIRE |
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