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Related Experiment Video

Updated: Jan 7, 2026

Author Spotlight: Exploring Autism Spectrum Disorder Symptoms in Fruit Flies — Genetic Models and Behavioral Tests

Author Spotlight: Exploring Autism Spectrum Disorder Symptoms in Fruit Flies — Genetic Models and Behavioral Tests

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Explainable Cluster-Based Predictive Framework for Early Diagnosis of Autism Spectrum Disorder Using Behavioral

Menwa Alshammeri^1,2, Zulfiqar Ahmad³, Mamoona Humayun⁴

¹Department of Computer Science, College of Computer and Information Sciences, Jouf University, Sakaka 72388, Saudi Arabia.

Diagnostics (Basel, Switzerland)

|December 30, 2025

Summary

This summary is machine-generated.

This study introduces an explainable AI framework for early Autism Spectrum Disorder (ASD) diagnosis using toddler behavioral data. The random forest model achieved 98.85% accuracy, identifying key indicators for timely intervention.

Keywords:

Autism Spectrum Disorder behavioral biomarkers clustering early screening explainable AI machine learning neuropsychiatric diagnosis

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Area of Science:

Neuroscience
Artificial Intelligence
Developmental Psychology

Background:

Autism Spectrum Disorder (ASD) presents early behavioral irregularities challenging timely diagnosis.
Limited diagnostic resources and complex manifestations hinder early detection.

Purpose of the Study:

To develop an explainable machine learning framework for early ASD diagnosis.
To utilize behavioral biomarkers from toddler screening data for improved detection.

Main Methods:

Integrated unsupervised learning (DBSCAN, K-means) for pattern identification.
Applied predictive models: logistic regression (LR), random forest (RF), support vector machine (SVM).
Employed SHAP analysis for model transparency and clinical interpretability.

Main Results:

The random forest (RF) model achieved the highest accuracy at 98.85%.
Support Vector Machine (SVM) and Logistic Regression (LR) models showed 97.70% and 90.53% accuracy, respectively.
Explainability analysis identified clinically relevant behavioral indicators for ASD risk.

Conclusions:

The framework enhances diagnostic accuracy for early ASD detection.
Promotes interpretable AI for integration into clinical neuropsychiatric assessment pipelines.