Development of a depression in Parkinson's disease prediction model using machine learning

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; Neuropsychological test; Rapid eye movement sleep behavior disorders; Risk factor; Supervised Machine Learning; Support vector machine.

Figure 1

The distribution of neuropsychological tests. CDR: Clinical Dementia Rating; K-MMSE: Korean Mini-Mental State Examination; K-MoCA: Korean-Montreal Cognitive Assessment; K-IADL: Korean Instrumental Activities of Daily Living; UPDRS: Untitled Parkinson`s Disease Rating total score; H and Y: Hoehn and Yahr; ADL: Activities of Daily Living.