{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,13]],"date-time":"2026-05-13T13:27:00Z","timestamp":1778678820925,"version":"3.51.4"},"reference-count":47,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,5,13]],"date-time":"2026-05-13T00:00:00Z","timestamp":1778630400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Public Health"],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Developmental disabilities affect approximately 1 in 6 children aged 3\u201317\u202fyears. The diagnostic process typically spans 2\u20133\u202fyears and disproportionately affects underserved populations. Traditional machine learning approaches for screening have demonstrated promising accuracy but often lack the transparency required for clinical acceptance.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We developed and validated an Adaptive Neuro-Fuzzy Inference System (ANFIS) using a longitudinal observational dataset of 5,000 children aged 1\u20136\u202fyears 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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      All four assessment domains differed significantly between diagnostic groups (\n                      <jats:italic>p<\/jats:italic>\n                      \u202f&amp;lt;\u202f0.001): Cognitive Scores (60.86\u202f\u00b1\u202f14.51 vs. 51.58\u202f\u00b1\u202f17.09), Behavioral Scores (55.82\u202f\u00b1\u202f9.38 vs. 49.64\u202f\u00b1\u202f11.21), Motor Skills (51.08\u202f\u00b1\u202f7.79 vs. 45.08\u202f\u00b1\u202f8.00), and Social Interaction (51.20\u202f\u00b1\u202f11.66 vs. 42.13\u202f\u00b1\u202f11.19). Family history was present in 85.6% of diagnosed children vs. 45.0% of typically developing children (\n                      <jats:italic>p<\/jats:italic>\n                      \u202f&amp;lt;\u202f0.001). The ANFIS model achieved 96.0% accuracy, 87.5% sensitivity, 97.6% specificity, and AUC\u202f=\u202f0.925 on a held-out test set of 100 cases. Five-fold cross-validation yielded a mean accuracy of 89.2%\u202f\u00b1\u202f3.4% (95% CI: [85.8, 92.6%]). Cognitive_Social_Ratio was the most influential diagnostic indicator.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fpubh.2026.1691449","type":"journal-article","created":{"date-parts":[[2026,5,13]],"date-time":"2026-05-13T12:38:31Z","timestamp":1778675911000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Explainable AI for early developmental disability detection: a neuro-fuzzy approach"],"prefix":"10.3389","volume":"14","author":[{"given":"Adel Saber","family":"Alanazi","sequence":"first","affiliation":[{"name":"College of Education, Jouf University","place":["Sakakah, Saudi Arabia"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sohil","family":"Alqazlan","sequence":"additional","affiliation":[{"name":"College of Education, Imam Mohammad Ibn Saud Islamic University (IMSIU)","place":["Riyadh, Saudi Arabia"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rayan","family":"Alanazi","sequence":"additional","affiliation":[{"name":"College of Computer and Information Sciences, Jouf University","place":["Sakakah, Saudi Arabia"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Houcine","family":"Benlaria","sequence":"additional","affiliation":[{"name":"College of Business, Jouf University","place":["Sakakah, Saudi Arabia"]}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1965","published-online":{"date-parts":[[2026,5,13]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.15585\/mmwr.ss7202a1","article-title":"Prevalence and Characteristics of Autism spectrum Disorder among children aged 8 Years\u2014Autism and Developmental Disabilities Monitoring Network, 11 sites, United States, 2020","volume":"72","author":"Maenner","year":"2023","journal-title":"MMWR Surveill Summ"},{"key":"ref2","doi-asserted-by":"publisher","first-page":"e20190811","DOI":"10.1542\/peds.2019-0811","article-title":"Prevalence and trends of developmental disabilities among children in the United States: 2009\u20132017","volume":"144","author":"Zablotsky","year":"2019","journal-title":"Pediatrics"},{"key":"ref3","doi-asserted-by":"publisher","first-page":"CD009260","DOI":"10.1002\/14651858.CD009260.pub3","article-title":"Early intensive behavioral intervention (EIBI) for young children with autism spectrum disorders (ASD)","volume":"2018","author":"Reichow","year":"2018","journal-title":"Cochrane Database Syst Rev"},{"key":"ref4","doi-asserted-by":"publisher","first-page":"112","DOI":"10.1080\/21678421.2023.2273361","article-title":"Racial disparities in ALS diagnostic delay: a single center\u2019s experience and review of potential contributing factors","volume":"25","author":"Chen","year":"2024","journal-title":"Amyotroph Lateral Scler Frontotemporal Degener"},{"key":"ref5","doi-asserted-by":"publisher","first-page":"190","DOI":"10.1136\/bmjqs-2024-017506","article-title":"Experiences with diagnostic delay among underserved racial and ethnic patients: a systematic review of the qualitative literature","volume":"34","author":"Faugno","year":"2025","journal-title":"BMJ Qual. 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