TY - JOUR AU - Swarts, Romano AU - Fourie, Pieter Rousseau AU - van den Heever, Dawie PY - 2019/11/26 Y2 - 2024/03/28 TI - ADHD Screening Tool: Investigating the effectiveness of a tablet-based game with machine learning JF - Global Health Innovation JA - GHI VL - 2 IS - 2 SE - Research articles DO - 10.15641/ghi.v2i2.809 UR - https://journals.uct.ac.za/index.php/GHI/article/view/809 SP - AB - <p>This study investigated the ability of a low-cost, mobile device-based game incorporating machine learning to screen participants between ages six and twelve years for attention-deficit/hyperactivity disorder inattentive subtype (ADHD<sub>I</sub>). Relevant information from literature was incorporated into the game in light of the DSM-V diagnostic criteria. The game has seven back-to-back segments with unique layouts and a visual theme.The ADHD Screening Tool presents a novel patient-testing interface with a cloud-based machine learning classifier (MLC) integrated with a consensus algorithm. The game was tested with 39 clinically diagnosed participants (ADHD<sub>I</sub>and non-ADHD). Out of nine classifiers tested, the locally-deep support vector machine gave the best results: using leave-one-out cross-validation, this MLC classified data from five game segments for 38 participants with sensitivity of 92.9% and specificity of 82.9%. By making use of the consensus algorithm, the 39th participant was correctly classified according to the clinical diagnosis. The MLC and consensus algorithm were able to classify 39 participants with a sensitivity of 100% and specificity of 87.5%. To overcome participant class imbalance, the synthetic minority oversampling technique (SMOTE) was implemented on game segment data. The SMOTE two-class LDSVM yielded sensitivity of 90.7% and specificity of 94.4%. The study used an internet-connected, commercially available tablet.</p> ER -