Authors : K. Sushma; Balasankula Srihitha; Bandari Bhavani; Eslavath Pavan Kumar

Volume/Issue : Volume 11 - 2026, Issue 3 - March

Google Scholar : https://tinyurl.com/2fwdbbzc

Scribd : https://tinyurl.com/bdfx7vbj

DOI : https://doi.org/10.38124/ijisrt/26mar1921

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : There is a significant Autism Spectrum Disorder (ASD). affect communication, socialization and reaction to. environmental stimuli. Timely diagnosis of ASD assists in giving. help and counsel to the injured persons on the right. time. Conven- tional diagnosis methods usually demand in-depth. consideration and assistance of professionals. This paper recommendsthe use of a machine learning model to assist in early screening of. ASD in terms of behavioral and demographic variables. Various random forest, decision tree, and other methods of classification. XGBoost, are trained and tested upon the customary preprocessing. of data, such as the processing of missing data and data conversion. categorical variables. The effectiveness of the different techniques is evaluated. The results indicate that ensemble methods are. more precise and valid than other methods.

Keywords : Autism Spectrum Disorder (ASD), Machine Learning, Early Screening, Behavioral Data, Demographic Data, Decision Tree, Random Forest, XGBoost, Data Preprocessing, Classification Algorithms, Predictive Modeling.

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