[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123181-en":3,"doc-seo-123181-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},123181,13056703019404,"Miles","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","An Ensemble Features Aware Machine Learning Model for Detection and Staging of Dyslexia - integrated user-friendly tool","A dyslexia detection and staging approach is presented using an artificial-intelligence–based testing workflow and an ensemble feature aware machine-learning model. Learners suspected of dyslexia complete quizzes and task-based assessments, and the resulting scores and time measurements are fed into an EFAM-XGB model to predict dyslexia. The method supports real-world imbalanced datasets and delivers strong diagnostic performance, reporting 98.7% accuracy on a dyslexia dataset, while enabling instructional activity suggestions for parents and teachers.","An ensemble features aware machine learning model for detection and staging of dyslexia  \nSailaja Mulakaluri1,2, Girisha Gowdra Shivappa2  \n1Department of Computer Science, St. Francis De Sales College, Bengaluru, India 2Department of Computer Science and Engineering, Dayananda Sagar University, Bengaluru, India  \nArticle history:  \nReceived Nov 23, 2023 Revised Feb 21, 2024 Accepted Mar 13, 2024  \nKeywords:  \nDeep learning Dyslexia  \nData imbalance Feature importance Learning disorder Machine learning  \nCorresponding Author:  \nDyslexia is a specific learning disorder (SLD) which may affect young child's cognitive skills, text comprehension, reading-writing and also problemsolving abilities. To diagnose and identify dyslexia, the testing scale tool has been proposed using artificial intelligence technique. The proposed tool allows the student who is suspected to have dyslexia to take up quiz and perform certain task based on the type of learning impairments. After completion of the test, resultant data is provided as input to the proposed ensemble feature aware machine-learning (EFAM) XGBoost (XGB) model. Based on the student assessment score and time taken by children, the EFAMXGB algorithm predicts dyslexia. The proposed EFAM-XGB is used to develop an integrated and user-friendly tool that is highly accurate in identifying reading disorders even with presence of realistic imbalanced dataset and suggest the most appropriate instructional activities to parents and teachers. The EFAM-XGB-based dyslexia detection method achieves very good accuracy of 98.7% for dyslexia dataset; thus, attain better performance in comparison with existing machine learning (ML)-based methodologies.  \nThis is an open access article under the CC BY-SA license.  \nSailaja Mulakaluri  \nDepartment of Computer Science and Engineering, Dayananda Sagar University Bengaluru, India  \nEmail: [sailaja.mulakaluri07@gmail.com](sailaja.mulakaluri07@gmail.com)  \nArticle Info ABSTRACT  \n1. INTRODUCTION  \nDyslexia, a specific learning disorder (SLD), is a condition characterized by neurobiological factors that impact individuals worldwide, affecting approximately 5-15% of the overall worldwide population [1] . Individuals diagnosed with dyslexia experience challenges in the areas of writing and reading, which are not influenced by factors such as intelligence, native language, socioeconomic status, or educational background. Moreover, individuals who possess knowledge of their dyslexia diagnosis have the potential to acquire and implement various coping strategies aimed at mitigating the adverse impacts associated with this condition [2],[3] . Nevertheless, it has been observed that individuals diagnosed with dyslexia tend to experience academic challenges if they do not receive adequate assistance. According to recent data, a significant proportion of individuals, specifically 35%, discontinue their education prematurely. Furthermore, it has been projected that just a small fraction, just over two percent, of individuals diagnosed with dyslexia successfully attain an undergraduate degree [4] .  \nIdentifying dyslexia poses a significant challenge, particularly in the context of Indian native languages characterized by transparent orthographies. In languages characterized by shallow orthographies, the relationship between graphemes (letters) and phonemes (sounds) tends to exhibit a higher level of consistency compared to spoken languages using deep orthographies, like English. Consequently, individuals with dyslexia encounter greater difficulties in acquiring reading skills within the context of English [5], [6] .  \nDue to the difficulties in diagnosing dyslexia in languages with clear orthographies and the less serious nature of its symptoms, dyslexia is therefore referred to as a \"hidden disability\" [6] . The present diagnostic and screening procedures necessitate the involvement of trained individuals who administer an extensive in-person assessment [7], [8]. This ","cbCaie6B9jyy3Tl2","https://ap.wps.com/l/cbCaie6B9jyy3Tl2","pdf",553986,1,10,"English","en",105,"# Introduction\n## Dyslexia background and challenges\n## Machine learning for dyslexia diagnosis\n## Problems in ML: data imbalance and feature importance","[{\"question\":\"What data does the EFAM-XGB model use to predict dyslexia?\",\"answer\":\"It uses student assessment scores and the time taken by children during the quiz and task-based testing.\"},{\"question\":\"How does the proposed approach address data imbalance?\",\"answer\":\"It is designed to work with realistic imbalanced datasets, using appropriate handling so the model remains accurate in dyslexia detection.\"},{\"question\":\"What performance does the EFAM-XGB dyslexia detection method achieve?\",\"answer\":\"The reported accuracy is 98.7% on the dyslexia dataset, outperforming existing machine-learning methodologies.\"}]","An Ensemble Features Aware Machine Learning Model for Detection and Staging of Dyslexia - integrated user-friendly tool | PDF",1785815071,25,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"an-ensemble-features-aware-machine-learning-model-for-detection-and-staging-of-dyslexia-integrated-user-friendly-tool","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/an-ensemble-features-aware-machine-learning-model-for-detection-and-staging-of-dyslexia-integrated-user-friendly-tool/123181/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What data does the EFAM-XGB model use to predict dyslexia?","Question",{"text":75,"@type":76},"It uses student assessment scores and the time taken by children during the quiz and task-based testing.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the proposed approach address data imbalance?",{"text":80,"@type":76},"It is designed to work with realistic imbalanced datasets, using appropriate handling so the model remains accurate in dyslexia detection.",{"name":82,"@type":73,"acceptedAnswer":83},"What performance does the EFAM-XGB dyslexia detection method achieve?",{"text":84,"@type":76},"The reported accuracy is 98.7% on the dyslexia dataset, outperforming existing machine-learning methodologies.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":21,"slug":133},"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]