[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117541-en":3,"doc-seo-117541-105":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},117541,2336464648746,"Skyler","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",7,"Healthcare","Early Prediction of Dementia Using Machine Learning","This study explores the application of machine learning algorithms for the early prediction of dementia, aiming to improve diagnostic accuracy and reliability. Utilizing a comprehensive dataset from Kaggle, which includes both continuous and categorical variables, four machine learning models—Random Forest, Decision Tree, Logistic Regression, and Support Vector Machine (SVM)—were implemented and evaluated. The study identifies cognitive test scores, the APOE ε4 allele, and depression status as key predictors of dementia. Tree-based models demonstrated superior performance, achieving perfect scores across accuracy, recall, precision, and F1. Limitations include reliance on a single dataset, limited predictors, and real-world validation challenges, with recommendations for larger and more diverse data.","35  \nCARITAS UNIVERSITY AMORJI-NIKE, EMENE, ENUGU STATE  \nCaritas Journal of Physical and Life Sciences  \nCJPLS, Volume 3, Issue 2 (2024)  \nArticle History: Received: 12th October, 2024 Revised: 4th December, 2024 Accepted: 10th December, 2024  \nEarly Prediction of Dementia Using Machine Learning  \nChizoba Nneka Ezeaku-Ezeme  \nDepartment of Data Science, Leeds Beckett University. UK  \n[C.Ezeaku-Ezeme2975@student.leedsbeckett.ac.uk](C.Ezeaku-Ezeme2975@student.leedsbeckett.ac.uk), [chizzyobyno@gmail.com](chizzyobyno@gmail.com);  \nOmankwu, Obinnaya Chinecherem Beloved  \nDepartment of Computer Science, Michael Okpara University of Agriculture, . Umudike  \n[saintbeloved@yahoo.com](saintbeloved@yahoo.com)  \nAbstract  \nThis study explores the application of machine learning algorithms for the early prediction of dementia, aiming to improve diagnostic accuracy and reliability. Utilizing a comprehensive dataset from Kaggle, which includes both continuous and categorical variables, four machine learning models—Random Forest, Decision Tree, Logistic Regression, and Support Vector Machine (SVM)—were implemented and evaluated. The study identifies cognitive test scores, the APOE ε4 allele, and depression status as key predictors of dementia. Tree-based models demonstrated superior performance, achieving perfect scores across metrics such as accuracy, recall, precision, and F1. Despite these promising results, the study acknowledges limitations such as the reliance on a single dataset, limitedpredictors, and challenges in real-world validation. Future research should incorporate larger, more diverse datasets, longitudinal data, and additional predictors to improve model robustness and applicability. These findings highlight the potential of machine learning as a transformative tool in clinical settings for timely dementia diagnosis and intervention.  \nKeywords Dementia, Machine Learning, Early Prediction, Cognitive Tests, Tree-based Models  \nIntroduction  \nDementia is a progressive neurological condition characterized by cognitive decline that impairs daily functioning. The global prevalence of dementia is rising, with projections suggesting over 135 million cases by 2050. Early detection is critical to managing the disease, delaying its progression, and improving patient outcomes. Traditional diagnostic methods, including cognitive tests and neuroimaging, have limitations such as invasiveness, cost, and time consumption. For example, magnetic resonance imaging (MRI) and computed tomography (CT) scans are effective but expensive and not always accessible, particularly in resource-limited settings. Additionally, cognitive tests often rely on subjective evaluation, which can lead to inconsistent results depending on the assessor's expertise.  \nRecent advancements in machine learning (ML) provide innovative, non-invasive alternatives for early dementia detection. ML algorithms can process vast amounts of data to identify subtle patterns that human observation might miss, making it possible to predict dementia earlier and more accurately. The integration of ML in clinical diagnostics has the potential to revolutionize the field by offering cost-effective, scalable solutions.  \nCARITAS UNIVERSITY JOURNALS [www.caritasuniversityjournals.org](www.caritasuniversityjournals.org)  \n36  \nThis study focuses on implementing and evaluating various ML algorithms to identify the most effective techniques for early dementia prediction. By leveraging a comprehensive dataset, this research aims to explore key predictors of dementia, evaluate the performance of different models, and propose recommendations for future research and clinical applications.  \nDementia imposes significant economic, psychological, and social burdens on patients and their families. According to the World Health Organization (WHO), the annual global cost of dementia reached $1 trillion in 2021, with projections suggesting this figure will double by 2030. Caregivers often experience emo","cbCaidYzZZccNAxM","https://ap.wps.com/l/cbCaidYzZZccNAxM","pdf",585058,1,6,"English","en",105,"# Introduction\n## Objectives of the Study\n## Literature Review","[{\"question\":\"Which machine learning models were used for early dementia prediction?\",\"answer\":\"The study implemented Random Forest, Decision Tree, Logistic Regression, and Support Vector Machine (SVM), then evaluated their performance for early-stage dementia prediction.\"},{\"question\":\"What predictors did the study identify as important for dementia risk?\",\"answer\":\"Key predictors included cognitive test scores, the APOE ε4 allele, and depression status.\"},{\"question\":\"How did the models perform, and what were the main limitations?\",\"answer\":\"Tree-based models achieved perfect results across accuracy, recall, precision, and F1. Limitations include using a single Kaggle dataset, a restricted set of predictors, and difficulties validating performance in real-world settings.\"}]","Early Prediction of Dementia Using Machine Learning | PDF",1785676802,15,{"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},"early-prediction-of-dementia-using-machine-learning","",{"@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/healthcare/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/early-prediction-of-dementia-using-machine-learning/117541/",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},"Which machine learning models were used for early dementia prediction?","Question",{"text":75,"@type":76},"The study implemented Random Forest, Decision Tree, Logistic Regression, and Support Vector Machine (SVM), then evaluated their performance for early-stage dementia prediction.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What predictors did the study identify as important for dementia risk?",{"text":80,"@type":76},"Key predictors included cognitive test scores, the APOE ε4 allele, and depression status.",{"name":82,"@type":73,"acceptedAnswer":83},"How did the models perform, and what were the main limitations?",{"text":84,"@type":76},"Tree-based models achieved perfect results across accuracy, recall, precision, and F1. 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