[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118102-en":3,"doc-seo-118102-105":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},118102,5909877438554,"Maeve","https://ap-avatar.wpscdn.com/avatar/5600025385ad2bf12a7?_k=1778553567797529272",8,"Research & Report","Towards Accessible Healthcare: Machine Learning-Enabled Diagnosis of Alzheimer’s Disease - Master of Science Thesis","Alzheimer’s disease creates a growing public-health burden as populations age worldwide. This thesis examines whether machine learning can deliver a reliable, cost-effective, and non-invasive alternative to conventional biomarker tests. Two approaches are proposed: MRI-based classification using hippocampal and related region volumes, and blood-based early diagnosis through efficient feature selection and scoring. Experiments test multiclass and binary settings across two datasets, reporting high diagnostic accuracies and demonstrating clinical viability for accessible screening.","Towards Accessible Healthcare: Machine Learning-Enabled Diagnosis of Alzheimer’s Disease  \nby  \nAsif Rasheed  \nA THESIS  \nSUBMITTED TO THE DEPARTMENT OF COMPUTER SCIENCE AND THE FACULTY OF GRADUATE STUDIES OF LAKEHEAD UNIVERSITY  \nIN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF  \nMASTER OF SCIENCE (SPECIALIZATION IN ARTIFICIAL  \nINTELLIGENCE)  \n© Copyright 2024 by Asif Rasheed Lakehead University  \nThunder Bay, ON, Canada  \nii  \nSupervisory Committee  \n\n| Dr. Zubair Fadlullah\u003Cbr>Supervisor\u003Cbr>(External Adjunct Professor, Department of Computer Science, Lakehead University, Thunder Bay, Ontario, Canada.\u003Cbr>Associate Professor, Department of Computer Science, University of Western Ontario, London, Ontario, Canada.) |\n| --- |\n| Dr. Mostafa Fouda\u003Cbr>Co-Supervisor\u003Cbr>(Associate Professor, Department of Electrical and Computer Engineering, Idaho State University, Pocatello, Idaho, USA.) |\n| Dr. Garima Bajwa\u003Cbr>Internal Examiner\u003Cbr>(Assistant Professor, Department of Computer Science, Lakehead University, Thunder Bay, Ontario, Canada.) |\n\nDr. Mohamed Ibrahem  \nExternal Examiner  \n(Assistant Professor, School of Computer and Cyber Sciences, Augusta University, Augusta, Georgia, USA.)  \niii  \nABSTRACT  \nAlzheimer’s disease poses a critical challenge to public health with an increasing prevalence among the aging population worldwide. The research question is whether machine learning-based solutions could be a reliable, cost-effective, and non-invasive alternative to existing biomarker tests. This thesis presents two machine learning-based approaches to diagnosing Alzheimer’s disease using Magnetic Resonance Imaging (MRI) and blood-based biomarkers. The first approach aims to train machine learning models on volumes of brain regions from MRI to classify patients into three classes: Alzheimer’s Dementia (AD), Mild Cognitive impairment (MCI) and Normal Control (NL) . Pretrained weights of a well-known CNN-based brain segmentation model were used in segmenting the hippocampal, parahippocampal, ventricles, entorhinal and cerebral white matter from MRI of patients, and their volumes were estimated. The volumes and demographic data of the patients were subsequently trained on SVM and KNN models, and their performance was recorded.  \nThe second approach aims to design efficient feature selection methods to identify relevant feature panels to identify individuals in the early stages of Alzheimer’s accurately. Two feature selection methods were introduced. The first method ranks features according to their dependence on the diagnosis, determined using metrics such as Mutual Information, Symmetric Uncertainty and Cramer’s V. Panels are formed in this method by iteratively selecting the top features and increasing the panel size. The second method filters out irrelevant features using the Euclidean distance between the class means of each feature and applying a threshold. Candidate panels identified using the two methods are extensively tested on two datasets, and their performances are reported. The blood-based approach is further expanded to allow multiclass classification of patients into three classes: AD, MCI and NL. While the first feature selection method remains largely unchanged, three new scoring techniques are used in the Euclidean distance-based approach to allow this. These approaches include scoring features using the minimum, maximum and average Euclidean distances between the class means for each feature. All possible combinations of relevant features identified using the two methods are extensively tested using two datasets, and their performances are reported.  \nFinally, we surveyed previous works demonstrating the significance of near-infrared as anon-invasive and accessible tool for diagnosing Alzheimer’s disease. In conclusion, the two approaches introduced in this thesis offer efficient and accurate diagnosis of Alzheimer’s disease. The first approach achieved an accuracy of 99 .74%, and the second achieved an acc","cbCain3pAl4BvIu0","https://ap.wps.com/l/cbCain3pAl4BvIu0","pdf",1865676,1,70,"English","en",105,"# Abstract\n# Supervisory Committee\n# Acknowledgements\n# Publications\n# Contents","[{\"question\":\"What is the main research goal of this thesis?\",\"answer\":\"To evaluate whether machine learning can provide a reliable, cost-effective, and relatively non-invasive diagnostic alternative to existing biomarker tests for Alzheimer’s disease.\"},{\"question\":\"How does the MRI-based approach classify patients?\",\"answer\":\"It uses a CNN-based brain segmentation model to estimate volumes of specific brain regions from MRI, then trains SVM and KNN models using these volumes and demographic data to classify patients into AD, MCI, and NL.\"},{\"question\":\"What are the two feature-selection strategies in the blood-based approach?\",\"answer\":\"One ranks features by dependence on diagnosis using metrics such as Mutual Information, Symmetric Uncertainty, and Cramer’s V; the other filters features using Euclidean distance between class means with thresholding and adds scoring via minimum, maximum, and average distances.\"}]","Towards Accessible Healthcare: Machine Learning-Enabled Diagnosis of Alzheimer’s Disease - Master of Science Thesis | PDF",1785681636,176,{"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},"towards-accessible-healthcare-machine-learning-enabled-diagnosis-of-alzheimers-disease-master-of-science-thesis","",{"@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/towards-accessible-healthcare-machine-learning-enabled-diagnosis-of-alzheimers-disease-master-of-science-thesis/118102/",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","2026-08-02",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 is the main research goal of this thesis?","Question",{"text":75,"@type":76},"To evaluate whether machine learning can provide a reliable, cost-effective, and relatively non-invasive diagnostic alternative to existing biomarker tests for Alzheimer’s disease.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the MRI-based approach classify patients?",{"text":80,"@type":76},"It uses a CNN-based brain segmentation model to estimate volumes of specific brain regions from MRI, then trains SVM and KNN models using these volumes and demographic data to classify patients into AD, MCI, and NL.",{"name":82,"@type":73,"acceptedAnswer":83},"What are the two feature-selection strategies in the blood-based approach?",{"text":84,"@type":76},"One ranks features by dependence on diagnosis using metrics such as Mutual Information, Symmetric Uncertainty, and Cramer’s V; 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