Is the Random Forest Algorithm Suitable for Predicting Parkinson's Disease with Mild Cognitive Impairment out of Parkinson's Disease with Normal Cognition?

Haewon Byeon

doi:10.3390/ijerph17072594

Abstract

Because it is possible to delay the progression of dementia if it is detected and treated in an early stage, identifying mild cognitive impairment (MCI) is an important primary goal of dementia treatment. The objectives of this study were to develop a random forest-based Parkinson’s disease with mild cognitive impairment (PD-MCI) prediction model considering health behaviors, environmental factors, medical history, physical functions, depression, and cognitive functions using the Parkinson’s Dementia Clinical Epidemiology Data (a national survey conducted by the Korea Centers for Disease Control and Prevention) and to compare the prediction accuracy of our model with those of decision tree and multiple logistic regression models. We analyzed 96 subjects (PD-MCI = 45; Parkinson’s disease with normal cognition (PD-NC) = 51 subjects). The prediction accuracy of the model was calculated using the overall accuracy, sensitivity, and specificity. Based on the random forest analysis, the major risk factors of PD-MCI were, in descending order of magnitude, Clinical Dementia Rating (CDR) sum of boxes, Untitled Parkinson’s Disease Rating (UPDRS) motor score, the Korean Mini Mental State Examination (K-MMSE) total score, and the K- Korean Montreal Cognitive Assessment (K-MoCA) total score. The random forest method achieved a higher sensitivity than the decision tree model. Thus, it is advisable to develop a protocol to easily identify early stage PDD based on the PD-MCI prediction model developed in this study, in order to establish individualized monitoring to track high-risk groups.

Highlights

Over the past decade, the field of geriatrics has experienced emerging interest in Parkinson’s disease with mild cognitive impairment (PD-MCI) [1,2,3,4]
The study reported that 84% of PD patients had cognitive impairment, and 50% of them progressed to PD dementia (PDD)
The objectives of this study were to develop a random forest-based PD-MCI prediction model considering health behaviors, environmental factors, medical history, physical functions, depression, and cognitive functions by using the Parkinson’s Dementia Clinical Epidemiology Data, and to compare its prediction of accuracy with those of decision tree and multiple logistic regression models

Summary

Introduction

The field of geriatrics has experienced emerging interest in Parkinson’s disease with mild cognitive impairment (PD-MCI) [1,2,3,4]. The study reported that 84% of PD patients had cognitive impairment, and 50% of them progressed to PD dementia (PDD). The mild cognitive impairment (MCI) stage is the earliest at which we can detect dementia [6]. Because it is possible to delay the progression of dementia when it is detected and treated in an early stage, identifying MCI is an important primary goal of dementia treatment [6]. The sociodemographic and neuropsychological characteristics of PD-MCI are less well-known than those of MCI and vascular mild cognitive impairment (vascular-MCI) [7,8].

Objectives

Methods

Results

Discussion

Conclusion