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The main aim of the current study was to find the most important indicators of dyslexia according to parents\u2019 reports and statements. First, a list of parent reports on dyslexia was developed. Then, according to the DSM-5 criteria (by clinicians), children were divided into two categories: children with dyslexia and healthy controls. Then, four Machine Learning (ML) algorithms\u2014Logistic Regression, Random Forest, Extreme Gradient Boosting (XGBoost), and ensemble methods\u2014were used to extract the most relevant predictors. To predict dyslexia, recursive feature elimination chose the five most important variables from 35 parent-reported items. Logistic Regression, Random Forest, XGBoost, and ensemble models were used in R-Studio. The ensemble model was the best. The most important were \u201cWord Guessing,\u201d \u201cLetter Confusion,\u201d \u201cLetter\u2013Sound Association,\u201d \u201cSlow Reading,\u201d and \u201cLetter Order Reversal.\u201d The study revealed that ML models can accurately identify dyslexia by analyzing parent-reported indicators. The five key predictors \u201cWord Guessing,\u201d \u201cLetter Confusion,\u201d \u201cLetter\u2013Sound Association,\u201d \u201cSlow Reading,\u201d and \u201cLetter Order Reversal\u201d provide essential information for detecting dyslexia early.<\/jats:p>","DOI":"10.1177\/10731911251329992","type":"journal-article","created":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T07:54:54Z","timestamp":1743062094000},"page":"178-190","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":3,"title":["Using Advanced Machine Learning Models for Detection of Dyslexia Among Children By Parents: A Study from Screening to Diagnosis"],"prefix":"10.1177","volume":"33","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7733-6931","authenticated-orcid":false,"given":"Abdullah","family":"Alrubaian","sequence":"first","affiliation":[{"name":"Qassim University, Buraydah, Saudi Arabia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2025,3,27]]},"reference":[{"key":"e_1_3_3_2_1","doi-asserted-by":"publisher","DOI":"10.1002\/9781119421566"},{"key":"e_1_3_3_3_1","doi-asserted-by":"publisher","DOI":"10.1590\/0103-166X2015000300004"},{"key":"e_1_3_3_4_1","volume-title":"Package \u2018randomforest\u2019","author":"Breiman L.","year":"2018","unstructured":"Breiman L., Cutler A., Liaw A., Wiener M. 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