Published online Nov 19, 2020. doi: 10.5498/wjp.v10.i11.245
Peer-review started: July 21, 2020
First decision: September 17, 2020
Revised: September 27, 2020
Accepted: October 11, 2020
Article in press: October 11, 2020
Published online: November 19, 2020
Despite the frequent progression from Parkinson’s disease (PD) to Parkinson dementia, the basis to diagnose early-onset Parkinson dementia (EOPD) in the early stage is still insufficient.
It is limited to develop a highly-reliable model to predict EOPD using individual indicators such as PD symptoms and neuropsychological tests. In order to develop an accurate prediction model, it is necessary to develop a comprehensive model that includes sociodemographic indices, Parkinson’s motor symptoms, Parkinson’s non-motor symptoms, rapid eye movement (REM) sleep behavior disorder, and neuropsychological indices.
The objectives of our study were to explore the prediction accuracy of sociodemographic factors, Parkinson’s motor symptoms, Parkinson’s non-motor symptoms, and REM sleep disorder for diagnosing EOPD using PD multicenter registry data.
This study was performed by analyzing the Parkinson’s Disease Epidemiology multicenter registry data provided by the National Biobank of Korea. This study analyzed 342 Parkinson patients (66 EOPD patients and 276 PD patients with normal cognition, younger than 65 years). The EOPD prediction model was developed using a random forest algorithm and the accuracy of the developed model was compared with the naive Bayesian model and discriminant analysis.
When the factors of EOPD were compared using “normalized importance of variables”, the Korean Mini Mental State Examination score was the most important factor of EOPD. Also, the accuracy of random decision forest was higher than that of naive Bayesian model and that of discriminant analysis.
It is believed that using random forest will increase accuracy while exploring major variables allowing us to predict EOPD, compared to traditional statistical techniques such as discriminant analysis.
It is necessary to develop a customized screening test that can early detect EOPD using biomarkers or genetic big data.