Dementia prediction in the general population using clinically accessible variables - a proof-of-concept study using machine learning

Aug 4, 2026

Upload by Eliana

Early identification of dementia is essential for timely intervention in high-risk individuals within the general population. Existing prognostic models require updating because external validation has revealed limitations in calibration and performance. Machine learning-based approaches are still emerging for dementia prediction and may improve predictive accuracy. This study compared supervised machine learning algorithms with logistic and Cox regression and evaluated the feasibility of using only clinically accessible predictors instead of MRI markers.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages12

Reading Time30 min

AccessFree to download

Abstract

Background

Methods

Results

Conclusions

Keywords

Introduction

Twait etal. BMC Medical Informatics and Decision Making (2023) 23:168 [https://doi.org/10.1186/s12911-023-02244-x](https://doi.org/10.1186/s12911-023-02244-x) BMC Medical Informatics and Decision Making RESEARCH Open Access Dementia prediction in the general population using clinically accessible variables: a proof-of-concept study using machine learning. The AGES-Reykjavik study Emma L. Twait 1,2,3,4, Constanza L. Andaur Navarro 1, Vil munur Gudnason5,6, Yi-Han Hu7, Lenore J. Launer7 and Mirjam I. Geerlings 1,3,4,7,8* Abstract Background Early identification of dementia is crucial for prompt intervention for high-risk individuals in the general population. External validation studies on prognostic models for dementia have highlighted the need for updated models. The use of machine learning in dementia prediction is in its infancy and may improve predictive performance. The current study aimed to explore the difference in performance of machine learning algorithms compared to traditional statistical techniques, such as logistic and Cox regression, for prediction of all-cause dementia. Our secondary aim was to assess the feasibility of only using clinically accessible predictors rather than MRI predictors. Methods Data are from 4,793 participants in the population-based AGES-Reykjavik Study without dementia or mild cognitive impairment at baseline (mean age: 76 years,% female: 59%) . Cognitive, biometric, and MRI assessments (total: 59 variables) were collected at baseline, with follow-up of incident dementia diagnoses for a maximum of 12 years. Machine learning algorithms included elastic net regression, random forest, support vector machine, and elastic net Cox regression. Traditional statistical methods for comparison were logistic and Cox regression. Model 1 was fit using all variables and model 2 was after feature selection using the Boruta package. A third model explored performance when leaving out neuroimaging markers (clinically accessible model) . Ten-fold cross-validation, repeated ten times, was implemented during training. Upsampling was used to account for imbalanced data. Tuning parameters were optimized for recalibration automatically using the caret package in R. Results 19% of participants developed all-cause dementia. Machine learning algorithms were comparable in performance to logistic regression in all three models. However, a slight added performance was observed in the elastic net Cox regression in the third model (c = 0 . 78, 95% CI: 0 .78–0. 78) compared to the traditional Cox regression (c = 0 . 75, 95% CI: 0 .74–0. 77) . Conclusions Supervised machine learning only showed added benefit when using survival techniques. Removing MRI markers did not significantly worsen our model’s performance. Further, we presented the use of a nomogram *Correspondence: Mirjam I. Geerlings[m.i.geerlings@amsterdamumc.nl](m.i.geerlings@amsterdamumc.nl) Full list of author information is available at the end of the article © The Author(s) 2023. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit [http://creativecommons.org/l](http://creativecommons.org/l)icenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ([http://creativecommons.org/p

FAQs

Why is early dementia identification important in this study?

Early identification enables prompt intervention for people at higher risk in the general population and supports better outcomes for high-risk individuals.

Which prediction approaches were compared?

The study compared supervised machine learning algorithms (elastic net regression, random forest, support vector machine, and elastic net Cox regression) with traditional logistic and Cox regression models.

Can clinically accessible variables replace MRI markers for dementia prediction?

Removing MRI markers did not significantly worsen model performance, supporting the feasibility of clinically accessible predictors.

Aug 4, 2026

Upload by Eliana