ArticleBMC medical research methodology2022
Dementia risk prediction in individuals with mild cognitive impairment: a comparison of Cox regression and machine learning models.
Article in BMC medical research methodology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.
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Who cites it
17 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Limited generalizability and high risk of bias in multivariable models predicting conversion risk from mild cognitive impairment to dementia: A systematic review.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2025Pooled it
- Mild cognitive impairment-to-Alzheimer's dementia progression risk: the contribution of the Interceptor project.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2026Article
- Deep learning to predict future cognitive decline: a multimodal approach using brain MRI and clinical data.Frontiers in neuroimaging · 2026Article
- Feature Selection and Model Optimization for Survival Prediction in Patients with Angina Pectoris.Journal of clinical medicine · 2025Article
- Assessing the accuracy of survival machine learning and traditional statistical models for Alzheimer's disease prediction over time: a study on the ADNI cohort.BMC medical research methodology · 2025Article
- Real-world prediction of early-onset dementia by health record data: A multi-center machine learning study.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2025Article
- Development of a deep learning model for survival prediction in heart failure: competing risk and frailty model.Scientific reports · 2025Article
- Interpretable Machine Learning for Cross-Cohort Prediction of Motor Fluctuations in Parkinson's Disease.Movement disorders : official journal of the Movement Disorder Society · 2025Article
- Multi-cohort machine learning identifies predictors of cognitive impairment in Parkinson's disease.NPJ digital medicine · 2025Article
- Article
- A Multivariable Prediction Model for Mild Cognitive Impairment and Dementia: Algorithm Development and Validation.JMIR medical informatics · 2024Article
- Understanding machine learning applications in dementia research and clinical practice: a review for biomedical scientists and clinicians.Alzheimer's research & therapy · 2024Review
- The natural course of binge-eating disorder: findings from a prospective, community-based study of adults.Psychological medicine · 2024Article
- Survival prediction and prognostic factors in colorectal cancer after curative surgery: insights from cox regression and neural networks.Scientific reports · 2023Article
- Dementia prediction in the general population using clinically accessible variables: a proof-of-concept study using machine learning. The AGES-Reykjavik study.BMC medical informatics and decision making · 2023Article
- Association between mental health symptoms and behavioral performance in younger vs. older online workers.Frontiers in psychiatry · 2023Article
- Predicting incident dementia in cerebral small vessel disease: comparison of machine learning and traditional statistical models.Cerebral circulation - cognition and behavior · 2023Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
backgroundCox proportional hazards regression models and machine learning models are widely used for predicting the risk of dementia. Existing comparisons of these models have mostly been based on empirical datasets and have yielded mixed results. This study examines the accuracy of various machine learning and of the Cox regression models for predicting time-to-event outcomes using Monte Carlo simulation in people with mild cognitive impairment (MCI).
methodsThe predictive accuracy of nine time-to-event regression and machine learning models were investigated. These models include Cox regression, penalized Cox regression (with Ridge, LASSO, and elastic net penalties), survival trees, random survival forests, survival support vector machines, artificial neural networks, and extreme gradient boosting. Simulation data were generated using study design and data characteristics of a clinical registry and a large community-based registry of patients with MCI. The predictive performance of these models was evaluated based on three-fold cross-validation via Harrell's concordance index (c-index), integrated calibration index (ICI), and integrated brier score (IBS).
resultsCox regression and machine learning model had comparable predictive accuracy across three different performance metrics and data-analytic conditions. The estimated c-index values for Cox regression, random survival forests, and extreme gradient boosting were 0.70, 0.69 and 0.70, respectively, when the data were generated from a Cox regression model in a large sample-size conditions. In contrast, the estimated c-index values for these models were 0.64, 0.64, and 0.65 when the data were generated from a random survival forest in a large sample size conditions. Both Cox regression and random survival forest had the lowest ICI values (0.12 for a large sample size and 0.18 for a small sample size) among all the investigated models regardless of sample size and data generating model.
conclusionCox regression models have comparable, and sometimes better predictive performance, than more complex machine learning models. We recommend that the choice among these models should be guided by important considerations for research hypotheses, model interpretability, and type of data.
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