Objectives
The overall aim of the present study was to explore the role of Cognitive Reserve (CR) in the conversion from Mild Cognitive Impairment (MCI) to dementia. We used traditional and Machine Learning (ML) techniques to compare converter and non‐converter participants. We also discuss the predictive value of CR proxies in relation to the ML model performance.
Methods
In total, 169 participants completed the longitudinal study. Participants were divided into a control group and three MCI subgroups, according to the Petersen criteria for diagnosis. Information about the participants was compared using 9 ML classification techniques. Seven relevant performance metrics were computed in order to evaluate the accuracy of prediction regarding converter and non‐converter participants.
Results
ML algorithms applied to socio‐demographic, basic health and CR proxy data enabled prediction of conversion to dementia. The best performing models were the Gradient Boosting Classifier (ACC= 0.93; F1=0.86 and Cohen’s kappa=0.82) and Random Forest Classifier (ACC= 0.92; F1=0.79 and Cohen’s kappa=0.71). Use of ML techniques corroborated the protective role of CR as a mediator of conversion to dementia, whereby participants with more years of education and higher vocabulary scores survived longer without developing dementia.
Conclusions
We used ML approaches to explore the role of CR in conversion from MCI to dementia. The findings indicate the potential value of ML algorithms for detecting risk of conversion to dementia in cognitive aging and CR studies. Further research is required to develop an ML‐based procedure that can be used to make robust predictions.