Abstract :

Autism Spectrum Disorder (ASD) Is A Complex Neurodevelopmental Disorder That Impacts Communication, Behavior, And Social Interaction, Typically Manifesting During Early Childhood. Early Identification Of ASD Is Critical, As Timely Intervention Can Significantly Improve Developmental Outcomes And Quality Of Life. This Study Proposes A Machine Learning-based Framework For The Automated And Early Detection Of ASD Using Behavioral And Demographic Data. The Dataset Comprises Responses To Standardized Autism Screening Questionnaires Combined With Relevant Demographic Features. Data Preprocessing Involves Managing Missing Values, Applying Label Encoding To Categorical Variables, And Scaling Numerical Attributes To Ensure Consistency And Enhance Model Accuracy.The Proposed Framework Employs An Ensemble Learning Approach That Integrates Random Forest, XGBoost, And Support Vector Machine (SVM) Classifiers Through A Soft Voting Mechanism To Achieve Improved Prediction Accuracy, Stability, And Generalization. The Optimized Ensemble Model And Its Preprocessing Pipeline Are Deployed Through A User-friendly Web Application Built Using The Flask Framework. This Interactive Platform Enables Users To Enter Screening Data And Receive Real-time Predictions Indicating The Likelihood Of ASD.By Leveraging Machine Learning And Web-based Technologies, This System Provides An Accessible, Efficient, And Cost-effective Tool For Preliminary ASD Detection. It Offers Valuable Support For Healthcare Professionals, Caregivers, And Families, Particularly In Regions With Limited Access To Specialized Diagnostic Resources. Index Terms: Autism Spectrum Disorder (ASD), Machine Learning, Ensemble Learning, Random Forest, XGBoost, Support Vector Machine (SVM), Early Detection, Flask Web Application, Behavioral Analysis, Neurodevelopmental Disorder.

Published:

29-10-2025

Issue:

Vol. 25 No. 10 (2025)

Page Nos:

371-379

Section:

Articles

License:

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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