• Swathi
• Harinithya
• Pardhasaradhi

Autism Spectrum Disorder is a neuro-developmental disorder, which may have lifelong consequences on the verbal communication, speech, cognitive, and social capabilities of a person. It is estimated that about 1 of 100 people worldwide are affected by it, and as a rule, developmental disorders manifest within the first two years after their birth. Notwithstanding the fact that the environmental and genetic aspects determine the actual disease, an early diagnosis and support are usually beneficial to the patients. In the present time, only assessing tools used during the sessions with the expert can contribute to the diagnosis of ASD. Besides taking diagnostic time, diagnosing ASD carries with it an increase in the costs of healthcare. To aid early identification, intervention, address concerns of delayed diagnosis and to improve the quality of life for individuals on autism spectrum, development of an efficient and accurate model for predicting autism spectrum disorder is required. This paper shows the approach of Machine Learning (ML) techniques to aid Autism detection. In this paper, we attempt to investigate the potential of how Naïve Bayes, Support Vector Machine, Logistic Regression, KNN, Artificial Neural Networks, and Convolutional Neural Networks can be utilized in ASD predicting and analyzation. The article will use behavioral data for research purposes as herein the paper looks at the performance of different machine learning algorithms used in ASD detection and a general comparison of the algorithms used.

Artificial Intelligence, Machine Learning, Behavioral Data, Autism Spectrum Disorder.

"Autism Detection Comparative Evaluation of Machine Learning Approaches", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 4, page no.d677-d680, April-2024, Available :http://www.jetir.org/papers/JETIR2404387.pdf

"Autism Detection Comparative Evaluation of Machine Learning Approaches", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 4, page no. ppd677-d680, April-2024, Available at : http://www.jetir.org/papers/JETIR2404387.pdf