# PANDAS: Paediatric Attention-Deficit/Hyperactivity Disorder Application Software
###### Abstract  
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. ADHD; screening; machine learning; SVM; children; novel +
Footnote †: journal: Medical  
Various methods exist, where objective diagnosis was attempted, with some degree of success. The first category of method is computerized. The most popular and efficient computerized approach is continuous performance testing (CPT). The Conners CPT 3 test [10] is a commercially available test that evaluates attention disorders and neurological functioning. Its aim is to provide an objective evaluation of individuals aged eight years and older. During the 14-minute-long assessment, subjects are to click whenever any letter except 'X' appears on the screen. Using a normative sample of 1400 subjects, representative of an American population, it was found that the classification accuracy was 83.90%, the sensitivity 86% and the specificity 81%. An example of a tool that integrates the Conners CPT 3 test in a more interactive manner is called MOXO. Instead of presenting the subject with the letter 'X', the subject must click whenever a specific face appears on the screen. Additional studies [11; 12; 13] have been done using MOXO to further validate the discriminatory ability of CPT testing. In these studies, comparisons were made between ADHD and non-ADHD groups with regards to the number of omission errors during the test. The difference between the studies was that different age groups were used, and different environmental distractors were used. It was found that when using MOXO, the sensitivity was between 81-91% (depending on the age group) and the specificity was between 85-89%. However, the studies did not focus on measuring the classification accuracy of MOXO and only used the sensitivity and specificity as the performance metrics. In addition to MOXO, it was also found that objective diagnosis of ADHD could be attempted using inertial sensors that were mounted to subjects and that captured data for one hour through various scenarios [14]. Classification was then done with a linear support vector machine (SVM) model. The results found in the study were that the classification accuracy was 95.12%, the specificity 95.65% and the sensitivity 94.44%. Additionally, there has been an FDA-approved device called NEBA that uses electroencephalogram signals to aid clinicians to make diagnoses. The most recent study [15] where NEBA was used was done in 2015. The aim of the study was to determine correlation between the diagnosis from NEBA and diagnosis from the consensus of a team of experts.