Toddler-Centered Case-Based Reasoning Framework for Early Diagnosis of Autism Spectrum Disorder

Abstract

Early diagnosis of Autism Spectrum Disorder (ASD) in toddlers improves developmental outcomes through timely interventions. However, traditional methods rely on subjective judgment, lengthy assessments, and limited specialist availability. This study introduces a toddler‑centered Case‑Based Reasoning (CBR) framework that emulates clinicians’ decision‑making by retrieving and adapting similar historical cases. The system processes screening data from children aged 12–36 months, including demographics, risk factors, and three core behavioral domains: social interaction, communication, and repetitive behaviors. Cases are organized hierarchically by age and severity. The framework implements the full CBR cycle: structured representation, similarity‑based retrieval via clinically weighted Euclidean distance, DSM‑5–inspired rule‑guided adaptation, and dynamic maintenance through validation and pruning. On a public toddler ASD screening dataset, it achieves 84.2% accuracy, 79.3% sensitivity, 89.7% specificity, and an F1‑score of 0.82 using similarity‑weighted voting over top‑k cases, outperforming Random Forest (81.5% accuracy) and k‑NN (78.9% accuracy) baselines trained on the same features. It also delivers transparent, interpretable recommendations via comparable cases, supporting clinician trust. This clinically aligned CBR system provides an accessible AI tool for early toddler ASD diagnosis in healthcare settings.