Abstract
This preliminary study mainly compared the performance for predicting mild cognitive impairment in Parkinson’s disease (PDMCI) between single machine learning and hybrid machine learning. This study analyzed 185 patients with Parkinson’s disease (75 Parkinson’s disease) patients with normal cognition, and 110 patients with PDMCI. PDMCI, an outcome variable, was divided into “with PDMCI” and “with normal cognition” according to the diagnosis of the neurologist. This study used 48 variables (diagnostic data), including motor symptoms of Parkinson’s disease, non-motor symptoms of Parkinson’s disease, and sleep disorders, as explanatory variables. This study developed seven machine learning models using blending (three hybrid models (polydot + C5.0, vanilladot + C5.0, and RBFdot + C5.0) and four single machine learning models (polydot, vanilladot, RBFdot, and C5.0)). The results of this study showed that the RBFdot + C5.0 was the model with the best performance to predict PDMCI in Parkinson’s disease patients with normal cognition (AUC = 0.88) among the seven machine learning models. We will develop interpretable machine learning using C5.0 in a follow-up study based on the results of this study.