Exploring Parkinson’s Disease Predictors based on Basic Intelligence Quotient and Executive Intelligence Quotient

Abstract

It is important to identify the risk factors of dementia and prevent them for the health of patients and caregivers. This study (1) explored sampling methods that could minimize overfitting due to data imbalance using a data-level approach, (2) developed nine ensemble learning models for predicting Parkinson s Disease–Mild Cognitive Impairment (PDMCI) ((undersampling, oversampling, and SMOTE) × (boosting, bagging, and random forest)=9), and (3) compared the accuracies, sensitivities, and specificities of these models to understand the prediction performance of the developed models. We examined 368 subjects: 320 healthy elderly people (≥60 and ≤74 years old) without Parkinson s disease (168 men and 152 women) and 48 subjects with PD-MCI (20 men and 28 women). This study used the Cognition Scale for Olde Adults (CSOA), which could measure cognitive functions comprehensively while considering age and education level, to determine the specific cognitive level of the subject. Our study developed nine prediction models ((undersampling, oversampling, and SMOTE) × (boosting, bagging, and random forest)=9) for developing a model to predict PD-MCI based on basic intelligence quotient and executive intelligence quotient. The analysis results showed that a random forest classifier with SMOTE had the best prediction performance with a sensitivity of 69.2%, a specificity of 75.7%, and a mean overall accuracy of 74.0%. In this final model, digit span test-backward, stroop test-interference trial, verbal memory test-delayed recall, verbal fluency test, and confrontation naming test were identified as the key variables with high weight in predicting PD-MCI. The results of this study implied that a random forest classifier with SMOTE could produce models with higher accuracy than a bagging classifier with SMOTE or a boosting classifier with SMOTE when analyzing imbalanced data. Keywords—Undersampling; oversampling; SMOTE; random forest; Parkinson s disease–mild cognitive impairment