However, there has been no study to develop a prediction model based on a nationwide epidemiological survey. In South Korea, previous community-based epidemiological studies on PD have been conducted on patients living in a single city. Therefore, it is necessary to develop an MCI prediction model reflecting the characteristics of the neuropsychological indices and lifestyles of the elderly in South Korea however, South Korea has less systematic epidemiological data on cognitive impairment in the elderly than other countries such as the United States and European countries. In particular, the development pattern and risk factors of cognitive impairment are known to vary according to race. Recent studies have pointed to the necessity of considering mental health, such as depression, while diagnosing MCI. Therefore, neuropsychological testing that also tests cognitive function has been used as an effective screening test for diagnosing MCI. However, this is unsuitable for early PD diagnosis, because brain atrophy can be confirmed visually only at a very advanced stage. It is possible to diagnose cerebrovascular diseases or to analyze brain atrophy using brain imaging. MCI can be diagnosed based on interviewing, cognitive function evaluation via a standardized neuropsychological test, and brain imaging such as magnetic resonance imaging (MRI). As a result, it is difficult to diagnose early stage PD. Additionally, people with PD experience a slowly deteriorating cognitive deficit and impaired motor function, which can be mistaken for cognitive frailty as part of the normal aging process. However, it is difficult to distinguish PD-MCI from MCI or vascular-MCI, because they show similar symptoms. The distinctive neuropsychological characteristics found in early stage PD-MCI are caused by executive function damage due to prefrontal hypofunction or malfunction. However, the sociodemographic and neuropsychological characteristics of PD-MCI are less well-known than those of MCI and vascular mild cognitive impairment (vascular-MCI). PD-MCI is frequently found in patients with PD. 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. The mild cognitive impairment (MCI) stage is the earliest at which we can detect dementia. Likewise, PD is often accompanied by cognitive dysfunction in addition to dyskinesia. The study reported that 84% of PD patients had cognitive impairment, and 50% of them progressed to PD dementia (PDD). The Sydney cohort study, the most highly representative epidemiology study on the subject, examined 136 patients diagnosed with Parkinson’s disease (PD) over 20 years. Over the past decade, the field of geriatrics has experienced emerging interest in Parkinson’s disease with mild cognitive impairment (PD-MCI). 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. The random forest method achieved a higher sensitivity than the decision tree model. 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 prediction accuracy of the model was calculated using the overall accuracy, sensitivity, and specificity.

We analyzed 96 subjects (PD-MCI = 45 Parkinson’s disease with normal cognition (PD-NC) = 51 subjects).

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. 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.

Random forest prediction