Predicting the Severity of Parkinson's Disease Dementia by Assessing the Neuropsychiatric Symptoms with an SVM Regression Model

Abstract

In this study, we measured the convergence rate using the mean-squared error (MSE) of the standardized neuropsychological test to determine the severity of Parkinson's disease dementia (PDD), which is based on support vector machine (SVM) regression (SVR) and present baseline data in order to develop a model to predict the severity of PDD. We analyzed 328 individuals with PDD who were 60 years or older. To identify the SVR with the best prediction power, we compared the classification performance (convergence rate) of eight SVR models (Eps-SVR and Nu-SVR with four kernel functions (a radial basis function (RBF), linear algorithm, polynomial algorithm, and sigmoid)). Among the eight models, the MSE of Nu-SVR-RBF was the lowest (0.078), with the highest convergence rate, whereas the MSE of Eps-SVR-sigmoid was 0.110, with the lowest convergence rate. The results of this study imply that this approach could be useful for measuring the severity of dementia by comprehensively examining axial atypical features, the Korean instrumental activities of daily living (K-IADL), changes in rapid eye movement sleep behavior disorder (RBD), etc. for optimal intervention and caring of the elderly living alone or patients with PDD residing in medically vulnerable areas.

Keywords: Parkinson’s disease dementia; clinical dementia rating; convergence rate; instrumental activities of daily living; neuropsychiatric symptoms; neuropsychological tests.

Figure 1

Density plots showing the distribution of the subjects’ neuropsychological test results: (a) Schwab and England activities of daily living (ADL) score, (b) Korean montreal cognitive assessment (K-MoCA) score, (c) Korean mini-mental state examination (K-MMSE) score, (d) Korean instrumental activities of daily living (K-IADL) score, (e) Unified Parkinson’s disease rating scale (UPDRS) (motor score), (f) UPDRS (total score), and (g) Hoehn and Yahr (H &Y) stage. The kernel density curve has a probability of 1 if all are added and the curves have been smoothed. The x-axis is the score for each test. Dark blue color = 50% highest density interval (HDI); green color = 95% HDI; red color = 99% HDI.

Figure 2

Five-fold cross-validation results of the dementia severity predictive model by the SVR algorithm. (a) epsilon-SVR (Eps-SVR)-linear, (b) Eps-SVR-polynomial, (c) Eps-SVR-radial basis function (RBF), (d) Eps-SVR-sigmoid, (e) Nu-SVR-linear, (f) Nu-SVR-polynomial, (g) Nu-SVR-RBF, and (h) Nu-SVR-sigmoid.

Figure 3

Functional weights of the major variables in the Nu-SVR-RBF model.