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

Abstract

Background/Objectives: Autism Spectrum Disorder (ASD) is a multifaceted neuropsychiatric condition characterized by early behavioral irregularities that often precede formal diagnosis. Timely and precise detection remains a major clinical challenge due to the complexity of behavioral manifestations and the limited accessibility of diagnostic resources. Methods: In this study, we present an explainable machine learning framework for the early diagnosis of ASD using behavioral biomarkers derived from toddler screening data. The framework integrates unsupervised learning (DBSCAN and K-means clustering) to identify latent behavioral patterns, followed by predictive modeling using logistic regression (LR), random forest (RF), and support vector machine (SVM). To ensure transparency and clinical interpretability, a SHAP (SHapley Additive exPlanations) analysis is employed to quantify the contribution of each behavioral feature to the model’s predictions. Results: Experimental evaluations reveal that the RF model achieves the highest accuracy (98.85%), followed by SVM (97.70%) and LR (90.53%). The explainability results highlight meaningful and clinically relevant behavioral indicators associated with ASD risk. Conclusions: The proposed framework not only enhances diagnostic accuracy but also promotes interpretable AI for real-world integration into neuropsychiatric assessment pipelines.