Screening of Autism Spectrum Disorder using Machine Learning Approach in Accordance with DSM-5

Autism Spectrum Disorder (ASD) is a specific category of neurodevelopmental disorder that can be associated with several behavioral conditions and has no known cure to date. ASD can be detected from a very early stage in childhood and upon successful detection can be ameliorated. There have been several clinical diagnosis procedures and they can be error-prone and time-consuming. Thus, machine learning-based prediction models for early-stage ASD as well as in adolescents and adults are being developed over the years. In our study, several parameters of ASD detection were implemented with open-source ASD datasets and analysed using several machine learning models like Logistic regression, XGboost, SVC and Naive Bayes. Among these XGboost showed the best performance. The outcomes of such analytical approaches demonstrate that, when suitably optimized, machine-learning techniques can offer robust predictions of Autism Spectrum Disorder (ASD) status. These findings imply that it may be feasible to employ these models for the early ASD detection, thereby enhancing the prospects of timely and effective intervention. XGBoost has given best results throughout all datasets, including cross validation. An accuracy of 100% has been achieved, making the model best for prediction.