[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119646-en":3,"doc-seo-119646-105":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},119646,13056703020460,"Valentina","https://ap-avatar.wpscdn.com/avatar/be000253dac470eee5d?_k=1778207105932848923",8,"Research & Report","Low-cost predictive models of dementia risk using machine learning and exposome predictors","Developing early dementia screening remains difficult and costly, delaying diagnosis for many patients. This study develops cost-effective screening methods using low-cost exposome predictors combined with machine learning to identify individuals at risk of dementia. Using UK Biobank data from 500,000 participants with matched dementia cases and controls, models are trained on imputed exposome factors and evaluated with internal and external validation, comparing logistic regression with XGBoost.","Health and Technology (2025) 15:355–365  \n[https://doi.org/10.1007/s12553-024-00937-5](https://doi.org/10.1007/s12553-024-00937-5)  \nORIGINAL PAPER  \nLow-cost predictive models of dementia risk using machine learning and exposome predictors  \nMarina Camacho1 · Angélica Atehortúa1 · Tim Wilkinson2,3 · Polyxeni Gkontra1 · Karim Lekadir1,4  \nReceived: 29 July 2024 / Accepted: 16 December 2024 / Published online: 21 December 2024 © The Author(s) 2024  \nAbstract  \nPurpose Diagnosing dementia, affecting over 55 million people globally, is challenging and costly, often leading to latestage diagnoses. This study aims to develop early, accurate, and cost-effective dementia screening methods using exposome predictors and machine learning. We investigate whether low-cost exposome predictors combined with machine learning models can reliably identify individuals at risk of dementia.  \nMethods We analyzed data from 500,000 UK Biobank participants, selecting 1523 diagnosed with dementia and an equal number of healthy controls, matched by age and sex. A total of 3046 participants were included: 2740 for internal validation and 306 for external validation. We used 128 low-cost exposome factors from baseline visits, imputed missing data, and assessed two predictive models: a classical logistic regression and a machine learning ensemble classifier (XGBoost) . Feature importance was estimated within the predictive models.  \nResults The XGBoost model outperformed the logistic regression model, achieving a mean AUC of 0.88 in external validation. We identified novel exposome factors that might be combined as potential markers for dementia, such as facial aging, the frequency of use of sun/ultraviolet light protection, and the length of mobile phone use.  \nConclusions Machine learning models utilizing exposome data can reliably identify individuals at risk of dementia, with XGBoost showing superior performance. This approach highlights the potential of low-cost, readily available exposome factors as markers for dementia. Future studies should validate these findings in diverse populations and explore the integration of additional exposome factors to enhance prediction accuracy.  \nKeywords Machine learning · Dementia · Risk prediction · Exposome predictors · Low-cost prediction  \n1 Introduction  \nDiagnosing dementia, a syndrome that currently affects more than 55 million people worldwide, remains a particularly challenging task [1, 2] . It involves undertaking several  \n􀀍 Marina Camacho [marinacamachosanz@ub.edu](marinacamachosanz@ub.edu)  \n1 Artificial Intelligence in Medicine Lab (BCN-AIM), Department de Matemàtiques i Informàtica, Universitat de Barcelona, Barcelona, Spain  \n2 Centre for Clinical Brain Sciences, The University of Edinburgh, Edinburgh EH16 4SB, UK  \n3 Usher Institute, The University of Edinburgh, Edinburgh EH16 4SB, UK  \n4 Institució Catalana de Recerca i Estudis Avançats (ICREA), Passeig Lluís Companys 23, Barcelona, Spain  \nexams such as cognitive tests (e.g., Montreal assessment), genetic tests (e.g. APOE) or brain scans to determine the degree of cognitive decline [3–6] . These procedures are associated with subjective evaluations, equipment related dependencies, elevated costs [7–10], as well as ethical concerns, such as socioeconomic and racial inequalities that can delay disease diagnosis [11] . As a result, patients are often diagnosed at a late stage when symptoms become highly pronounced [12], while treatments do not yet exist for dementia [13–15]. Therefore, in recent years, there has been a growing focus on early diagnosis, risk prediction and preventive interventions by means of reduction of modifiable risk factors [16, 17] . To this end, new low-cost, accessible and objective screening tools for identifying individuals at risk of dementia are essential.  \nSeveral predictive models for dementia risk have been proposed in the literature. For example, Mehmood et al. proposed a method for risk prediction based on brain i","cbCaic1njx0qIBxk","https://ap.wps.com/l/cbCaic1njx0qIBxk","pdf",1556197,1,11,"English","en",105,"# Introduction\n# Methods\n# Results\n# Conclusions\n## Feature importance","[{\"question\":\"What is the study’s main goal?\",\"answer\":\"To create early, accurate, and cost-effective dementia screening methods by combining low-cost exposome predictors with machine learning.\"},{\"question\":\"Which datasets and participant groups were used?\",\"answer\":\"The study used 500,000 UK Biobank participants, selecting 1,523 with dementia and an equal number of healthy controls matched by age and sex, yielding 3,046 participants total.\"},{\"question\":\"How did the machine learning model perform compared with logistic regression?\",\"answer\":\"The XGBoost model outperformed logistic regression, achieving a mean AUC of 0.88 in external validation.\"}]","Low-cost predictive models of dementia risk using machine learning and exposome predictors | PDF",1785725453,28,{"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},"low-cost-predictive-models-of-dementia-risk-using-machine-learning-and-exposome-predictors","",{"@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/low-cost-predictive-models-of-dementia-risk-using-machine-learning-and-exposome-predictors/119646/",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","2026-08-03",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’s main goal?","Question",{"text":75,"@type":76},"To create early, accurate, and cost-effective dementia screening methods by combining low-cost exposome predictors with machine learning.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which datasets and participant groups were used?",{"text":80,"@type":76},"The study used 500,000 UK Biobank participants, selecting 1,523 with dementia and an equal number of healthy controls matched by age and sex, yielding 3,046 participants total.",{"name":82,"@type":73,"acceptedAnswer":83},"How did the machine learning model perform compared with logistic regression?",{"text":84,"@type":76},"The XGBoost model outperformed logistic regression, achieving a mean AUC of 0.88 in external validation.","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},[93,97,101,105,110,115,120,123,128,131,135],{"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":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]