Browsing by Author "Mwamba, H. M."

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    PANDAS: Paediatric Attention-Deficit Hyperactivity/Disorder Application Software.
    (Stellenbosch : Stellenbosch University, 2018-12) Mwamba, H. M.; Fourie, P. R.; Van den Heever, David Jacobus; Stellenbosch University. Faculty of Engineering. Dept. of Mechanical and Mechatronic Engineering.
    ENGLISH ABSTRACT: Attention-deficit/hyperactivity disorder (ADHD) is a common neuropsychiatric disorder that impairs social, academic, and occupational functioning in children, adolescents and adults. It is estimated that approximately as high as 10% of South African children have ADHD. Some dilemmas are however present in terms of the treatment of the disorder: firstly, there are no risk-free methods for its treatment and secondly, no fully objective diagnostic assessments exist. To date, very few quantitative methods have been successfully implemented. It is therefore necessary to further investigate methods that objectively diagnose, treat, and manage the disorder. The aim of the study is thus to develop a novel method that can be used as an aid to provide screening of ADHD. The method proposed is the form of a tablet-based game with underlying algorithms. The objective of the method is to differentiate between an ADHD individual versus a non-ADHD individual, based on the way they play the game. A beta-testing phase was done and comprised of 30 children (19 non-ADHD and 11 ADHD) between the ages of 4 and 18 years old. The machine learning model that was used was linear support vector machine (SVM). Two datasets were used: 1) game-play dataset which included data such as task completion time and number of mistakes made and 2) accelerometer data set from the tri-axial accelerometer. A feature set was extracted from these two datasets and the best features were selected using sequential forward selection. These best features were then used for developing the classifier. A test set accuracy of 85.7% was achieved. Leave-one-out cross-validation (LOOCV) was performed and its accuracy was 83.5%. An overall classification accuracy of 86.5% was achieved. For the application of a screening tool, sensitivity was deemed an important metric and. The model achieved a sensitivity of 75% which was seen as acceptable. The results of the classifier were indicative that a quantitative tool could indeed be developed to screen for ADHD.