[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126333-en":3,"doc-seo-126333-105":30,"detail-sidebar-cat-0-en-105":92},{"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":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":11,"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},126333,962085570644,"Evangeline","https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d",8,"Research & Report","Performance of Dyslexia Dataset for Machine Learning Algorithms - Classification Results","Developmental dyslexia is a neurological learning disability marked by difficulties in reading and writing, affecting children’s education and social well-being. The study proposes a machine learning approach using EEG samples to extract discriminative patterns from a dyslexia dataset. Classification is evaluated with K-nearest neighbor, decision tree, linear discriminant analysis, and support vector machine. MATLAB-based experiments report SVM accuracy of 90.76%, with sensitivity of 89% for SVM and LDA specificity reported at 91.89%.","Performance of dyslexia dataset for machine learning  \nalgorithms  \nJ. Jincy1,2, P. Subha Hency Jose1  \n1Department of Biomedical Engineering, Faculty of Biomedical Engineering, Karunya Institute of Technology and Sciences,  \nCoimbatore, India  \n2Department of Electronics and Communication Engineering, CSI College of Engineering, Ketti, India  \nArticle history:  \nReceived Feb 22, 2024 Revised Jun 25, 2024 Accepted Jul 14, 2024  \nKeywords:  \nDyslexia  \nEEG  \nK nearest neighbor Machine learning SVM  \nCorresponding Author:  \nLearning disability is a condition usual amongst most populace due to poor phonological capability in humans making them impaired. One such neurological disorder is developmental dyslexia, a lack of reading and writing skills leading to difficulty in school education. The essential causes of developmental dyslexia are the consumption of more drug treatments during pregnancy, the over-the-counter purchase of medicines for minor ailments without the recommendation of physicians, and uncared-for head accidents during early life. The occurrence of this trouble is acute in India. Attempts were made by many to detect dyslexic children to reduce the intensity of this hassle. In this proposed effort, machine learning is used to locate significant styles characterizing people using EEG samples. A dataset is used for examination of developmental dyslexia, and classification is done using K nearest neighbor (KNN), decision tree, linear discriminant analysis (LDA), and support vector machine (SVM) to evaluate the performance. This piece of research work is done on MATLAB to provide results on simulation with classification accuracy of 90.76% for SVM, sensitivity of 89% for SVM, and LDA with 91.89% specificity for SVM providing optimum yield.  \nThis is an open access article under the CC BY-SA license.  \nP. Subha Hency Jose  \nDepartment of Biomedical Engineering, Faculty of Biomedical Engineering Karunya Institute of Technology and Sciences  \nCoimbatore, India  \n[Email: hency20002000@karunya.edu](Email: hency20002000@karunya.edu)  \nArticle Info ABSTRACT  \n1. INTRODUCTION  \nA neurological disorder termed dyslexia affects 5% to 15% of Indian kids, who are labeled as lazy because of their incapability in reading, writing, mathematic skills [1] . They yearn to be appreciated and accepted by society turning their life into social trauma. The learning disabilities like reading and writing curbs the life of young children's education and future endeavor. Genetic research shows that dyslexia is inherited from the family. Around 23-65% of children have taken over from the genes of their parentsunnoticeably. Learning disabilities are of predominant type’s dyslexia, dyscalculia and dysgraphia. Dyslexia is a selected problem related to information sounds and phrases due to a lack of phonological processing [2], Dysgraphia is related to deficiency in writing words and scripts highly difficult to decode [3], Dyscalculia isan arithmetic disorder that causes poor mathematical and logical capacity [4] .  \nThere are many standardized tests for analysis of dyslexia with regard to reading, writing, spelling abilities, mental caliber, and working memory. Glancing at the tests the severity of the infirmity can be identified. The weak linguistic abilities can be assessed using word test and questionnaire connected to  \nreading and writing based on clinical observation [5] . In addition to IQ tests neurobiological behavior in brain structure can be analyzed by using imaging tools and their behavior could be understood. Neural connectivity varies for dyslexic and normal children altering their brain pattern. Functional magnetic resonance imaging (FMRI) is a technique used to analyze word recognition based on changes in the blood flow in the frontal and occipital regions. The tests are based on images and words that are used regularly [6] . FMRI has been very beneficial it has drastic pitfalls that make the real neural pastime identification hard ","cbCaisxy7McRiYqC","https://ap.wps.com/l/cbCaisxy7McRiYqC","pdf",338634,6,1,"English","en",105,"# Article Info ABSTRACT\n## Introduction\n## EEG-based Machine Learning Classification\n## Methods and Models\n## Experimental Results and Performance","[{\"question\":\"What problem does the study address?\",\"answer\":\"The study targets developmental dyslexia, a learning disability that impairs reading and writing skills and affects education.\"},{\"question\":\"Which data source and features are used for classification?\",\"answer\":\"EEG samples are used to capture neurological signals, and machine learning identifies significant patterns for classification.\"},{\"question\":\"Which machine learning models are evaluated and what performance is reported?\",\"answer\":\"KNN, decision tree, LDA, and SVM are evaluated using MATLAB, reporting SVM accuracy 90.76%, SVM sensitivity 89%, and LDA specificity 91.89%.\"}]","Performance of Dyslexia Dataset for Machine Learning Algorithms - 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