Frontiers in public health · 2026
Explainable AI for early developmental disability detection: a neuro-fuzzy approach.
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Alanazi Adel Saber, Alqazlan Sohil, Alanazi Rayan, Benlaria Houcine
Аннотация
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 (p < 0.001): Cognitive Scores (60.86 ± 14.51 vs. 51.58 ± 17.09), Behavioral Scores (55.82 ± 9.38 vs. 49.64 ± 11.21), Motor Skills (51.08 ± 7.79 vs. 45.08 ± 8.00), and Social Interaction (51.20 ± 11.66 vs. 42.13 ± 11.19). Family history was present in 85.6% of diagnosed children vs. 45.0% of typically developing children (p < 0.001). The ANFIS model achieved 96.0% accuracy, 87.5% sensitivity, 97.6% specificity, and AUC = 0.925 on a held-out test set of 100 cases. Five-fold cross-validation yielded a mean accuracy of 89.2% ± 3.4% (95% CI: [85.8, 92.6%]). Cognitive_Social_Ratio was the most influential diagnostic indicator.
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.
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 (p < 0.001): Cognitive Scores (60.86 ± 14.51 vs. 51.58 ± 17.09), Behavioral Scores (55.82 ± 9.38 vs. 49.64 ± 11.21), Motor Skills (51.08 ± 7.79 vs. 45.08 ± 8.00), and Social Interaction (51.20 ± 11.66 vs. 42.13 ± 11.19). Family history was present in 85.6% of diagnosed children vs. 45.0% of typically developing children (p < 0.001). The ANFIS model achieved 96.0% accuracy, 87.5% sensitivity, 97.6% specificity, and AUC = 0.925 on a held-out test set of 100 cases. Five-fold cross-validation yielded a mean accuracy of 89.2% ± 3.4% (95% CI: [85.8, 92.6%]). Cognitive_Social_Ratio was the most influential diagnostic indicator.
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.