Creation of a machine learning-based model for early identification of mild cognitive impairment risk in Parkinson's disease: A PD research center-based population study

BackgroundThis study aimed to use data from the PD-MDCNC database to develop a risk prediction model using machine learning (ML) methods for the early identification of the risk of mild cognitive impairment in Parkinson's disease (PD-MCI) within a Parkinson's disease (PD) patients cohort.MethodsThis study used assessment scales and blood test results from 523 patients with Parkinson's disease (PD) in the PD-MDCNC database, collected from the Hubei Parkinson's Disease Clinical Research Center, to develop a predictive model for assessing the risk of mild cognitive impairment (MCI) in PD patients. Using simple assessment scales and blood test data, we developed ten machine learning algorithms to predict PD-MCI. The optimal model was determined through comparison, and its performance was evaluated using an external validation cohort of 139 PD patients from a Taihe state hospital in Shiyan City, Hubei Province.ResultsThe area under the receiver operating characteristic curve (AUC) for the ten models ranged from 0.57 to 0.72. The best predictive performance was achieved with RF (AUC=0.72). The variable importance ranking results indicated that the U3 score was the best important feature in predicting PD-MCI.ConclusionsThis study presents a robust machine learning model for the early detection of MCI in PD patients, which may help provide a simple method for early identification of PD-MCI patient.