ML Algorithms that have been Utilised to Classify Neuro-Developmental Disorders: A Review

Abstract

The Autism Diagnostic Interview (ADI)-revised is frequently used to aid in the repetitive behaviors that are unusual recognition of autism. Heredity and environment both have an impact on the emergence of neuro-developmental diseases like Autism Spectrum Disorder (ASD). For diagnosing ASDs, conventional clinical evaluations from the last few decades are still often used. These traditional methods rely on gathering a lot of information from the responses of many respondents and the severity of different behavioural features, which is then recognised by the researcher when constructing a diagnostic criterion. On the other hand, traditional diagnostic techniques have a high risk of generating an inaccurate diagnosis, which could lead to the unwarranted recommendation of long-lasting medicinal therapy. This could lead to a decline in functioning and a higher chance of developing other clinical and social issues. The focus of this work is on the implementation of Machine Learning (ML) technologies for the immediate treatment and identificaton of ASD symptoms. Despite the fact that many studies have been carried out, we still think that our review paper is pertinent and can help those researchers who are working on this subject by providing all they require in one location.

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