[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123786-en":3,"doc-seo-123786-105":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},123786,3848291630094,"Emma Wilson","https://eur-avatar.wpscdn.com/davatar_085a072bc5b1113ac321206ff7593b45",7,"Healthcare","Machine learning-based prediction of mild cognitive impairment among individuals with normal cognitive function","Machine learning is leveraged to forecast the future risk of mild cognitive impairment (MCI) in cognitively normal individuals, addressing the gap left by studies that primarily examine risk factors in MCI or dementia patients. A longitudinal retrospective dataset integrates brain MRI findings, clinical visits, and cognitive assessments separated by more than three years. Multiple models, including random forest, support vector machine, logistic regression, eXtreme Gradient Boosting, and naïve Bayes, are trained using combined clinical and imaging variables. eXtreme Gradient Boosting (XGB) yields the strongest classification performance, with high accuracy for clinical-image integration. White matter hyperintensity, especially in the frontal lobe, and systolic blood pressure control emerge as key predictors.","TYPE Original Research PUBLISHED 02 February 2024 DOI 10.3389/fneur.2024.1352423  \nOPEN ACCESS  \nEDITED BY  \nKeping Yu,  \nHosei University, Chiyoda, Japan  \nREVIEWED BY  \nYang Luoxiao,  \nCity University of Hong Kong, Hong Kong SAR, China Qiwen Deng,  \nNanjing Medical University, China  \n*CORRESPONDENCE  \nJin Jie Liu  \n [vip2ljj@163.com](vip2ljj@163.com)[ ](vip2ljj@163.com)Chao Huang  \n [chaohuang@ustb.edu.cn](chaohuang@ustb.edu.cn)  \n†These authors have contributed equally to this work and share last authorship  \nRECEIVED 08 December 2023  \nACCEPTED 15 January 2024  \nPUBLISHED 02 February 2024  \nCITATION  \nZhu XW, Liu SB, Ji CH, Liu JJ and Huang C (2024) Machine learning-based prediction of mild cognitive impairment among individuals with normal cognitive function.  \nFront. Neurol. 15:1352423 .  \ndoi: 10.3389/fneur.2024.1352423  \nCOPYRIGHT  \n© 2024 Zhu, Liu, Ji, Liu and Huang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.  \nMachine learning-based prediction of mild cognitive impairment among individuals with normal cognitive function  \nXia Wei Zhu 1, Si Bo Liu 2, Chen Hua Ji3, Jin Jie Liu3*† and Chao Huang 1*†  \n1School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing, China, 2 Intensive Care Unit, Dalian Municipal Central Hospital Affiliated Dalian University of Technology, Dalian, China, 3 Department of General Medicine, Dalian Municipal Central Hospital Affiliated to Dalian University of Technology, Dalian, China  \nBackground: Previous studies mainly focused on risk factors in patients with mild cognitive impairment (MCI) or dementia. The aim of the study was to provide basis for preventing MCI in cognitive normal populations.  \nMethods: The data came from a longitudinal retrospective study involving individuals with brain magnetic resonance imaging scans, clinical visits, and cognitive assessment with interval of more than 3 years. Multiple machinelearning technologies, including random forest, support vector machine, logistic regression, eXtreme Gradient Boosting, and naïve Bayes, were used to establish a prediction model of a future risk of MCI through a combination of clinical and image variables.  \nResults: Among these machine learning models; eXtreme Gradient Boosting (XGB) was the best classification model. The classification accuracy of clinical variables was 65.90%, of image variables was 79. 54%, of a combination of clinical and image variables was 94.32% . The best result of the combination was an accuracy of 94.32%, a precision of 96. 21%, and a recall of 93.08% . XGB with a combination of clinical and image variables had a potential prospect for the risk prediction of MCI. From clinical perspective, the degree of white matter hyperintensity (WMH), especially in the frontal lobe, and the control of systolic blood pressure (SBP) were the most important risk factor for the development of MCI.  \nConclusion: The best MCI classification results came from the XGB model with a combination of both clinical and imaging variables. The degree of WMH in the frontal lobe and SBP control were the most important variables in predicting MCI.  \nKEYWORDS  \ndementia, mild cognitive impairment, machine learning, random forest, eXtreme Gradient Boosting  \n1 Introduction  \nDementia is a syndrome characterized by significantly decreased cognitive function, daily living ability, and social function, which could be caused by various diseases with no reversible or curative treatment. In 2015, an estimated 47 million people age 65 and older were living with dementia, and the number might triple ","cbCaibuCiFkUWS9V","https://ap.wps.com/l/cbCaibuCiFkUWS9V","pdf",1674587,1,10,"English","en",105,"# Background\n# Methods\n# Results\n# Conclusion","[{\"question\":\"What is the study aiming to achieve?\",\"answer\":\"To provide a basis for preventing mild cognitive impairment by building a prediction/screening model for individuals who are cognitively normal.\"},{\"question\":\"What data sources and time window are used to train the prediction models?\",\"answer\":\"Brain MRI scans, clinical visits, and cognitive assessments collected in a longitudinal retrospective design with intervals of more than three years.\"},{\"question\":\"Which machine learning approach performed best and what were the key predictors?\",\"answer\":\"eXtreme Gradient Boosting (XGB) achieved the best performance when combining clinical and imaging variables; white matter hyperintensity in the frontal lobe and systolic blood pressure control were identified as most important.\"}]","Machine learning-based prediction of mild cognitive impairment among individuals with normal cognitive function | PDF",1785818553,25,{"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},"machine-learning-based-prediction-of-mild-cognitive-impairment-among-individuals-with-normal-cognitive-function","",{"@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/machine-learning-based-prediction-of-mild-cognitive-impairment-among-individuals-with-normal-cognitive-function/123786/",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 is the study aiming to achieve?","Question",{"text":75,"@type":76},"To provide a basis for preventing mild cognitive impairment by building a prediction/screening model for individuals who are cognitively normal.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What data sources and time window are used to train the prediction models?",{"text":80,"@type":76},"Brain MRI scans, clinical visits, and cognitive assessments collected in a longitudinal retrospective design with intervals of more than three years.",{"name":82,"@type":73,"acceptedAnswer":83},"Which machine learning approach performed best and what were the key predictors?",{"text":84,"@type":76},"eXtreme Gradient Boosting (XGB) achieved the best performance when combining clinical and imaging variables; 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