Leveraging Machine Learning for Early Autism Diagnosis: A Comprehensive Review of Algorithms, Techniques, and Future Directions

Autism disorder is a multifactorial neurological disorder which is difficult to diagnose because of the pervasive symptoms and small initial signs. Machine learning (ML) in this review changes the system of early SD diagnosis. It studies the variety of machine learning (ML) methods (SVM) and ensemble models, with their application to a wide range of data. It covers classical methods, like decision trees, modern ones like the deep neural networks (DNN) and gene profiles, neuroimaging and behavioral analysis. It is of vital importance to the entrance of multimodal data, the creation of explainable AI, and the appearance of hybrid models, which do improve the accuracy and transparency of the diagnosis. Topical concerns, such as interpretability of complex models, class imbalances, and small datasets, are also discussed in the paper providing recommendations on the future research. This is a piece of work that entails the unification of findings of diverse researches that could maximize the initial diagnosis and specific reactions of ASD patients with the focus of using ML.