Abstract
An early detection of the autism spectrum disorder is an element in favor of a better management of the autistic child and allows to expect better results in terms of independence and acquisition of social skills. It is therefore crucial to identify autism as early as possible. Usually, traditional techniques like questionnaires are used for autism screening. These methods relies mainly on the expertise and empirical knowledge of psychiatrists and are known to exaggerate results, leading to a high false positive rate. In this paper we address this problem by proposing a novel screening method for autism based on combining machine learning and questionnaire hybridization techniques in order to improve the screening accuracy. After testing several machine learning models including SVM and random forest trees on a locally collected dataset, an accuracy of 97.5% has been achieved. These results are promising and incite to deepen and concretize this study as well as its generalization on other neurodevelopmental disorders.
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Hadri, S.A., Bouramoul, A. (2023). Towards a Novel Machine Learning and Hybrid Questionnaire Based Approach for Early Autism Detection. In: Troiano, L., Vaccaro, A., Kesswani, N., Díaz Rodriguez, I., Brigui, I., Pastor-Escuredo, D. (eds) Key Digital Trends in Artificial Intelligence and Robotics. ICDLAIR 2022. Lecture Notes in Networks and Systems, vol 670. Springer, Cham. https://doi.org/10.1007/978-3-031-30396-8_5
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