On Effectively Predicting Autism Spectrum Disorder Using an Ensemble of Classifiers
This work addresses early screening for ASD in children, which is incremental as it applies existing ensemble methods to a specific dataset.
The paper tackled predicting autism spectrum disorder (ASD) using ensemble classifiers, finding that multiple classifier systems, particularly with three classifiers, outperformed individual classifiers, with bagging and boosting achieving excellent results on a dataset of 3,000 sessions from 61 children.
An ensemble of classifiers combines several single classifiers to deliver a final prediction or classification decision. An increasingly provoking question is whether such systems can outperform the single best classifier. If so, what form of an ensemble of classifiers (also known as multiple classifier learning systems or multiple classifiers) yields the most significant benefits in the size or diversity of the ensemble itself? Given that the tests used to detect autism traits are time-consuming and costly, developing a system that will provide the best outcome and measurement of autism spectrum disorder (ASD) has never been critical. In this paper, several single and later multiple classifiers learning systems are evaluated in terms of their ability to predict and identify factors that influence or contribute to ASD for early screening purposes. A dataset of behavioural data and robot-enhanced therapy of 3,000 sessions and 300 hours, recorded from 61 children are utilised for this task. Simulation results show the superior predictive performance of multiple classifier learning systems (especially those with three classifiers per ensemble) compared to individual classifiers, with bagging and boosting achieving excellent results. It also appears that social communication gestures remain the critical contributing factor to the ASD problem among children.