{"title":"探索和识别预测儿童阅读障碍的关键因素:从筛选到诊断的先进机器学习算法","authors":"Abdullah Alrubaian","doi":"10.1002/cpp.70077","DOIUrl":null,"url":null,"abstract":"<div>\n \n \n <section>\n \n <h3> Introduction</h3>\n \n <p>The current study aimed to develop and validate a machine learning (ML)–based predictive models for early dyslexia detection in children by integrating neurocognitive, linguistic and behavioural predictors.</p>\n </section>\n \n <section>\n \n <h3> Method</h3>\n \n <p>A cross-sectional study was conducted with 300 Saudi Arabian children (150 children with dyslexia, 150 controls) aged 6–12 years and their parents. Participants underwent assessments for attention, phonological awareness, rapid automatised naming (RAN), cognitive flexibility and other predictors. Four ML models—logistic regression, random forest, XGBoost and an ensemble—were trained and evaluated using performance metrics (AUC, sensitivity, specificity). Recursive feature elimination (RFE) identified key predictors.</p>\n </section>\n \n <section>\n \n <h3> Results</h3>\n \n <p>The RFE (15-fold cross-validation) 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: 0.92–0.98), 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%).</p>\n </section>\n \n <section>\n \n <h3> Conclusion</h3>\n \n <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-term impacts.</p>\n </section>\n </div>","PeriodicalId":10460,"journal":{"name":"Clinical psychology & psychotherapy","volume":"32 3","pages":""},"PeriodicalIF":3.2000,"publicationDate":"2025-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Exploring and Identifying Key Factors in Predicting Dyslexia in Children: Advanced Machine Learning Algorithms From Screening to Diagnosis\",\"authors\":\"Abdullah Alrubaian\",\"doi\":\"10.1002/cpp.70077\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div>\\n \\n \\n <section>\\n \\n <h3> Introduction</h3>\\n \\n <p>The current study aimed to develop and validate a machine learning (ML)–based predictive models for early dyslexia detection in children by integrating neurocognitive, linguistic and behavioural predictors.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Method</h3>\\n \\n <p>A cross-sectional study was conducted with 300 Saudi Arabian children (150 children with dyslexia, 150 controls) aged 6–12 years and their parents. Participants underwent assessments for attention, phonological awareness, rapid automatised naming (RAN), cognitive flexibility and other predictors. Four ML models—logistic regression, random forest, XGBoost and an ensemble—were trained and evaluated using performance metrics (AUC, sensitivity, specificity). Recursive feature elimination (RFE) identified key predictors.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Results</h3>\\n \\n <p>The RFE (15-fold cross-validation) 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: 0.92–0.98), 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%).</p>\\n </section>\\n \\n <section>\\n \\n <h3> Conclusion</h3>\\n \\n <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-term impacts.</p>\\n </section>\\n </div>\",\"PeriodicalId\":10460,\"journal\":{\"name\":\"Clinical psychology & psychotherapy\",\"volume\":\"32 3\",\"pages\":\"\"},\"PeriodicalIF\":3.2000,\"publicationDate\":\"2025-04-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Clinical psychology & psychotherapy\",\"FirstCategoryId\":\"102\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1002/cpp.70077\",\"RegionNum\":3,\"RegionCategory\":\"心理学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"PSYCHOLOGY, CLINICAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Clinical psychology & psychotherapy","FirstCategoryId":"102","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/cpp.70077","RegionNum":3,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"PSYCHOLOGY, CLINICAL","Score":null,"Total":0}
Exploring and Identifying Key Factors in Predicting Dyslexia in Children: Advanced Machine Learning Algorithms From Screening to Diagnosis
Introduction
The current study aimed to develop and validate a machine learning (ML)–based predictive models for early dyslexia detection in children by integrating neurocognitive, linguistic and behavioural predictors.
Method
A cross-sectional study was conducted with 300 Saudi Arabian children (150 children with dyslexia, 150 controls) aged 6–12 years and their parents. Participants underwent assessments for attention, phonological awareness, rapid automatised naming (RAN), cognitive flexibility and other predictors. Four ML models—logistic regression, random forest, XGBoost and an ensemble—were trained and evaluated using performance metrics (AUC, sensitivity, specificity). Recursive feature elimination (RFE) identified key predictors.
Results
The RFE (15-fold cross-validation) 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: 0.92–0.98), 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%).
Conclusion
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-term impacts.
期刊介绍:
Clinical Psychology & Psychotherapy aims to keep clinical psychologists and psychotherapists up to date with new developments in their fields. The Journal will provide an integrative impetus both between theory and practice and between different orientations within clinical psychology and psychotherapy. Clinical Psychology & Psychotherapy will be a forum in which practitioners can present their wealth of expertise and innovations in order to make these available to a wider audience. Equally, the Journal will contain reports from researchers who want to address a larger clinical audience with clinically relevant issues and clinically valid research.