Explainable AI for early developmental disability detection: a neuro-fuzzy approach.

Background: Developmental disabilities affect approximately 1 in 6 children aged 3-17 years. The diagnostic process typically spans 2-3 years and disproportionately affects underserved populations. Traditional machine learning approaches for screening have demonstrated promising accuracy but often lack the transparency required for clinical acceptance. Methods: We developed and validated an Adaptive Neuro-Fuzzy Inference System (ANFIS) using a longitudinal observational dataset of 5,000 children aged 1-6 years from 12 early childhood centers (4,311 typically developing; 689 with developmental disabilities, prevalence 13.8%). The ANFIS model implemented Gaussian membership functions and Sugeno fuzzy inference, incorporating age-adjusted ratios and cross-domain interaction features. Model performance was evaluated using standard binary classification metrics and five-fold stratified cross-validation. Results: All four assessment domains differed significantly between diagnostic groups ( Conclusion: The ANFIS approach demonstrates a clinically relevant balance between diagnostic accuracy and interpretability, positioning it as a viable clinical decision-support tool. Larger external validation studies across diverse populations are required before widespread implementation.