Dementia risk prediction in individuals with mild cognitive impairment: a comparison of Cox regression and machine learning models.

ABSTRACT:

Background

Cox 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).

Methods

The 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, an

SUBMITTER: Wang M 

PROVIDER: S-EPMC9628121 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature