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

Meng Wang, Matthew Greenberg, Nils D. Forkert, Thierry Chekouo, Gabriel Afriyie, Zahinoor Ismail, Eric E. Smith & Tolulope T. Sajobi
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...
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