Observational Study
Copyright ©The Author(s) 2020. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Psychiatr. Oct 19, 2020; 10(10): 234-244
Published online Oct 19, 2020. doi: 10.5498/wjp.v10.i10.234
Development of a depression in Parkinson's disease prediction model using machine learning
Haewon Byeon
Haewon Byeon, Major in Medical Big Data, College of AI Convergence, Inje University, Gimhae 50834, Gyeonsangnamdo, South Korea
Author contributions: Byeon H designed the research, and interpreted the data, performed the analysis, and wrote the manuscript.
Supported by the National Research Foundation of Korea, No. NRF-2019S1A5A8034211; and the National Research Foundation of Korea, No. NRF-2018R1D1A1B07041091.
Institutional review board statement: The study was approved by the Research Ethics Review Board of the National Biobank of Korea (No. KBN-2019-005) and the Korea CDC (No. KBN-2019-1327).
Informed consent statement: All patients gave informed consent.
Conflict-of-interest statement: No benefits in any form have been received or will be received from a commercial party related directly or indirectly to the subject of this article.
Data sharing statement: Technical appendix and statistical code are available from the corresponding author at bhwpuma@naver.com.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Haewon Byeon, DSc, PhD, Professor, Major in Medical Big Data, College of AI Convergence, Inje University, Major in Medical Big Data, College of AI Convergence, Inje University, Gimhae 50834, Gyeonsangnamdo, South Korea. bhwpuma@naver.com
Received: March 29, 2020
Peer-review started: March 30, 2020
First decision: August 22, 2020
Revised: September 1, 2020
Accepted: September 22, 2020
Article in press: September 22, 2020
Published online: October 19, 2020
Processing time: 205 Days and 15.5 Hours
ARTICLE HIGHLIGHTS
Research background

It is important to diagnose depression in Parkinson's disease (PD) patients as soon as possible and identify predictors of depression to improve the quality of life in PD patients.

Research motivation

It has been reported that the duration of PD, Hoehn and Yahr phase, age, activities of daily living, low cognitive function, and sleep behavior disorder affect depression in PD. However, these previous studies are limited in determining a risk factor, while considering multiple risk factors as each study used different confounding factors or covariates and used regression models to predict a risk factor, although they were effective in exploring individual risk factors.

Research objectives

The objectives of our study were to develop a model for predicting depression in Parkinson's disease (DPD) based on the support vector machine while considering sociodemographic factors, health habits, Parkinson's symptoms, sleep behavior disorders, and neuropsychiatric indicators as predictors and to provide baseline data for identifying DPD.

Research methods

The data used in this study was collected at 14 university hospitals from January to December, 2015, under the supervision of the Korea Centers for Disease Control. The data consisted of health behaviors, sociodemographic factors, motor characteristics related to PD, disease history, neuropsychological test results and sleep behavior disorders. 

Research results

When the effects of PD motor symptoms were compared using “functional weight”, late motor complications (occurrence of levodopa-induced dyskinesia) were the most influential risk factors for DPD.

Research conclusions

It is necessary to develop customized screening that can detect DPD in the early stage and continuously monitor high-risk groups based on the factors related to DPD derived from this predictive model in order to maintain the emotional health of PD patients. 

Research perspectives

It is also necessary to develop customized programs for managing depression from the onset of PD.