[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121802-en":3,"doc-seo-121802-105":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},121802,16904993612988,"Olivia Brown","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Identifying Alzheimer Disease Dementia Levels Using Machine Learning Methods - Paper","Dementia is a major neurodegenerative manifestation of Alzheimer’s disease (AD), and progression from mild to severe increasingly restricts independent daily functioning, making timely and accurate staging essential. This study proposes a machine-learning and deep-learning framework to classify four dementia stages using RF, SVM, and CNN, with watershed segmentation extracting features from MRI images. Experiments on the ADNI dataset show that SVM with watershed-derived features achieves the highest accuracy at 96.25%. Results indicate that integrating watershed segmentation improves model performance.","Identifying Alzheimer Disease Dementia Levels Using Machine Learning Methods  \nMd Gulzar Hussain  \nSchool of Computer and Artificial Intelligence, Changzhou University Changzhou, Jiangsu, China  \nEmail: [gulzar.ace@outlook.com](gulzar.ace@outlook.com)  \nYe Shiren  \nSchool of Computer and Artificial Intelligence, Changzhou University Changzhou, Jiangsu, China  \nEmail: [yes@cczu.edu.cn](yes@cczu.edu.cn)  \narXiv :2311 .01428v1 [ cs .LG] 2 Nov 2023  \nAbstract—Dementia, a prevalent neurodegenerative condition, is a major manifestation of Alzheimer’s disease (AD). As the condition progresses from mild to severe, it significantly impairs the individual’s ability to perform daily tasks independently, necessitating the need for timely and accurate AD classification. Machine learning or deep learning models have emerged as effective tools for this purpose. In this study, we suggested an approach for classifying the four stages of dementia using RF, SVM, and CNN algorithms, augmented with watershed segmentation for feature extraction from MRI images. Our results reveal that SVM with watershed features achieves an impressive accuracy of 96.25%, surpassing other classification methods. The ADNI dataset is utilized to evaluate the effectiveness of our method, and we observed that the inclusion of watershed segmentation contributes to the enhanced performance of the models.  \nKeywords—Alzheimer’s Disease, Dementia, CNN, Random Forest, SVM, MRI image, Computer-Aided Diagnostic.  \nI. INTRODUCTION  \nThe brain, being one of the highly vital and complicated organs in a human body, is responsible for a wide range of essential functions such as intellectual invention, problemsolving, thought, judgment, creativity, and memories. However, when an individual develops dementia, their cognitive abilities become impaired. The majority of all dementia sufferers have Alzheimer’s disease (AD), making it the most prevalent reason of dementia. AD gradually destroys brain cells, resulting in disconnection from surroundings, loss of recognition of loved ones, inability to recall childhood memories, familiar faces, and even basic procedures. Alarmingly, the number of individuals affected by AD is projected to increase significantly in the coming decades. It is estimated that by 2050, there will be over 150 million AD sufferers worldwide, up from 50 million in 2020 . This rapid increase in AD cases poses a significant burden on patients, families, and healthcare systems, unless there are advancements in prevention measures utilizing modern medical technologies. The need for research and innovation in AD prevention and treatment is crucial to address this growing global health challenge. Efforts to develop effective medications, leveraging contemporary medical technologies, are essential to combat the rising prevalence of AD and alleviate the impact it has on individuals, families, and healthcare systems worldwide.  \nAccording to the estimates, there are 32, 69, and 315 million people worldwide who have prodromal AD, AD dementia, and preclinical AD, respectively. Combined, they made up 416 million people on the AD spectrum or 22% of all those 50 and older [1] . The burden of dementia grew globally between 1990 and 2019, increasing by 147.95% and 160.84%, correspondingly, in frequency and prevalence. In order to deal with the rising incidence of dementia, we should pay focus to the elderly society, prioritize programs that focus on dementia health conditions, and create plans of action [2] . Considering millions of people struggling with dementia globally, the prevalence of dementia has a significant necessity. The indications of AD and those of vascular dementia (VD) or ordinary aging intersect, making the identification of AD problematic [3] . Premature and precise identification of Alzheimer’s disease (AD) is crucial for patient care, prevention, and treatment. Several research initiatives aim to utilize imaging of brain techniques, including the magnetic resona","cbCaipGdpMdWUfAi","https://ap.wps.com/l/cbCaipGdpMdWUfAi","pdf",2374802,1,9,"English","en",105,"# Introduction\n## Disease burden and need for early classification\n## Neuroimaging background\n## Motivation for MRI-based machine learning","[{\"question\":\"How are dementia stages for Alzheimer’s disease classified in this study?\",\"answer\":\"The method classifies four dementia stages using RF, SVM, and CNN models combined with watershed segmentation for MRI feature extraction.\"},{\"question\":\"Which model achieves the best performance and what accuracy is reported?\",\"answer\":\"SVM using watershed features achieves the highest reported accuracy of 96.25%.\"},{\"question\":\"What dataset is used to evaluate the approach?\",\"answer\":\"The ADNI dataset is used to assess the effectiveness of the proposed method.\"}]","Identifying Alzheimer Disease Dementia Levels Using Machine Learning Methods - Paper | PDF",1785806946,23,{"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},"identifying-alzheimer-disease-dementia-levels-using-machine-learning-methods-paper","",{"@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/identifying-alzheimer-disease-dementia-levels-using-machine-learning-methods-paper/121802/",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},"How are dementia stages for Alzheimer’s disease classified in this study?","Question",{"text":75,"@type":76},"The method classifies four dementia stages using RF, SVM, and CNN models combined with watershed segmentation for MRI feature extraction.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which model achieves the best performance and what accuracy is reported?",{"text":80,"@type":76},"SVM using watershed features achieves the highest reported accuracy of 96.25%.",{"name":82,"@type":73,"acceptedAnswer":83},"What dataset is used to evaluate the approach?",{"text":84,"@type":76},"The ADNI dataset is used to assess the effectiveness of the proposed method.","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},[93,97,101,105,110,115,120,123,127,130,134],{"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":21,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]