A Comparative Machine Learning Approach for Early Autism Spectrum Disorder Detection Using Behavioral Features

Sachin Kumar; Vishal Bharati; Ashutosh Kumar Rao

doi:10.36948/ijfmr.2026.v08i03.81553

A Comparative Machine Learning Approach for Early Autism Spectrum Disorder Detection Using Behavioral Features

Author(s)	Mr. Sachin Kumar, Mr. Vishal Bharati, Dr. Ashutosh Kumar Rao
Country	India
Abstract	ASD may be considered as a disorder characterized by challenges in social interaction, social communication, and repetitive behavior. Diagnosis of ASD at early stages is essential because of its huge influence on treatment and quality of life. Among some of the disadvantages related to the existing techniques of ASD diagnosis are their inefficiency, subjectivity, and the need for professional skills. The present research aims at constructing a comparative machine learning model based on the behavioral and demographic characteristics of ASD patients. In order to evaluate various types of machine learning models, supervised models such as random forest, support vector machine, and decision tree classifiers will be applied in various ASD datasets. Diverse data pre-processing methods will be utilized for the efficient implementation of the algorithms. Machine learning model performance is estimated based on its prediction accuracy measured by metrics such as Accuracy, Precision, Recall, and F1-Score. Random Forest algorithm was demonstrated as superior among others by achieving a remarkable accuracy of 92%.
Keywords	Autism Spectrum Disorder (ASD), Machine Learning, Random Forest, Support Vector Machine (SVM), Decision Tree, Behavioral Data Analysis, Early Detection, Classification, Healthcare Analytics, Artificial Intelligence
Field	Computer Applications
Published In	Volume 8, Issue 3, May-June 2026
Published On	2026-06-14
DOI	https://doi.org/10.36948/ijfmr.2026.v08i03.81553

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International Journal For Multidisciplinary Research

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A Comparative Machine Learning Approach for Early Autism Spectrum Disorder Detection Using Behavioral Features

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