{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,3]],"date-time":"2026-09-03T02:37:22Z","timestamp":1788403042464,"version":"build-2803163510"},"reference-count":45,"publisher":"Wiley","issue":"3","license":[{"start":{"date-parts":[[2025,4,30]],"date-time":"2025-04-30T00:00:00Z","timestamp":1745971200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2025,4,30]],"date-time":"2025-04-30T00:00:00Z","timestamp":1745971200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Clin Psychology and Psychoth"],"published-print":{"date-parts":[[2025,5]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>The current study aimed to develop and validate a machine learning (ML)\u2013based predictive models for early dyslexia detection in children by integrating neurocognitive, linguistic and behavioural predictors.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Method<\/jats:title>\n                    <jats:p>A cross\u2010sectional study was conducted with 300 Saudi Arabian children (150 children with dyslexia, 150 controls) aged 6\u201312\u2009years and their parents. Participants underwent assessments for attention, phonological awareness, rapid automatised naming (RAN), cognitive flexibility and other predictors. Four ML models\u2014logistic regression, random forest, XGBoost and an ensemble\u2014were trained and evaluated using performance metrics (AUC, sensitivity, specificity). Recursive feature elimination (RFE) identified key predictors.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The RFE (15\u2010fold cross\u2010validation) identified attention, RAN, early language delay, phonological awareness and cognitive flexibility as the top five predictors of dyslexia. The ML models demonstrated high diagnostic accuracy for dyslexia detection. Logistic regression achieved superior performance with an area under the curve (AUC) of 0.95 (95% CI: 0.92\u20130.98), sensitivity of 97%, specificity of 91% and overall accuracy of 94%. Random forest and XGBoost yielded slightly lower but robust AUCs (0.91 and 0.93, respectively), with balanced sensitivity (95%) and specificity (91%). The ensemble model harmonised algorithmic strengths, retaining an AUC of 0.93 while prioritising interpretability through weighted contributions from XGBoost (40%), random forest (30%) and logistic regression (30%).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>This study demonstrated the transformative potential of ML in dyslexia diagnostics. By systematically prioritising phonological awareness, RAN and attention deficits, ML models offer a scalable, objective framework for early identification. These tools could alleviate reliance on subjective assessments, enabling timely interventions to mitigate dyslexia's long\u2010term impacts.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/cpp.70077","type":"journal-article","created":{"date-parts":[[2025,4,30]],"date-time":"2025-04-30T20:02:17Z","timestamp":1746043337000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Exploring and Identifying Key Factors in Predicting Dyslexia in Children: Advanced Machine Learning Algorithms From Screening to Diagnosis"],"prefix":"10.1002","volume":"32","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7733-6931","authenticated-orcid":false,"given":"Abdullah","family":"Alrubaian","sequence":"first","affiliation":[{"name":"Department of Special Education, College of Education Qassim University  Buraydah Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2025,4,30]]},"reference":[{"key":"e_1_2_14_2_1","volume-title":"Manual for the Child Behavior Checklist and Revised Child Behavior Profile","author":"Achenbach T.","year":"1983"},{"key":"e_1_2_14_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ridd.2024.104812"},{"key":"e_1_2_14_4_1","doi-asserted-by":"publisher","DOI":"10.1002\/9781119421566"},{"key":"e_1_2_14_5_1","doi-asserted-by":"publisher","DOI":"10.1177\/10731911251329992"},{"key":"e_1_2_14_6_1","volume-title":"Package \u2018randomforest\u2019","author":"Breiman L.","year":"2018"},{"key":"e_1_2_14_7_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41398-019-0607-2"},{"key":"e_1_2_14_8_1","unstructured":"Chen T.2015.Xgboost: Extreme Gradient Boosting.R package version 0.4\u20102 1(4)."},{"issue":"1","key":"e_1_2_14_9_1","first-page":"40","article-title":"Package \u2018Xgboost\u2019","volume":"90","author":"Chen T.","year":"2019","journal-title":"R Version"},{"key":"e_1_2_14_10_1","doi-asserted-by":"crossref","unstructured":"Chen X.\u2010W. andJ. 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