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
Abstract
BACKGROUND

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

AIM

To develop a model for predicting 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 provide baseline data for identifying DPD.

METHODS

This study analyzed 223 of 335 patients who were 60 years or older with PD. Depression was measured using the 30 items of the Geriatric Depression Scale, and the explanatory variables included PD-related motor signs, rapid eye movement sleep behavior disorders, and neuropsychological tests. The support vector machine was used to develop a DPD prediction model.

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 Parkinson's symptoms.

CONCLUSION

It is necessary to develop customized screening tests 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.

Keywords: Depression in Parkinson's disease; Supervised Machine Learning; Neuropsychological test; Risk factor; Support vector machine; Rapid eye movement sleep behavior disorders

Core Tip: When the effects of Parkinson’s disease (PD) motor symptoms were compared using “functional weight”, the occurrence of levodopa-induced dyskinesia was the most influential risk factor in the diagnosis of depression in Parkinson’s disease (DPD). These results can be used as baseline information to prevent DPD and establish management strategies. It is necessary to develop customized screening tests that can detect DPD patients 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. It is also necessary to develop customized programs for managing depression from the onset of PD.