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

Table 3 Functional weight (importance of variables)

Variables	Functional weight
K-MMSE	-1.454
K-MoCA	-4.499
CDR (Global CDR score)	2.643
CDR (sum of boxes)	2.607
K-IADL	4.919
Total UPDRS	5.302
Motor UPDRS	0.978
H and Y staging	-2.190
Schwab and England ADL	-5.055
Age (60-74)	-3.638
Age (75+)	3.638
Gender (female)	0.345
Education (high school graduate and above)	1.150
Family PD history (yes)	0.232
Pack year (21-40)	0.032
Pack year (41-60)	0.000
Pack year (61+)	-1.032
Coffee (yes)	1.006
Pesticide exposure (currently not exposed but exposed previously)	-1.000
Pesticide exposure (currently exposed to pesticide)	2.264
Tremor (yes)	0.000
Rigidity (yes)	-1.000
Bradykinesia (yes)	0.675
Postural instability (yes)	1.117
LMC (levodopa-induced dyskinesia)	3.000
LMC (Both Wearing OFF and levodopa-induced dyskinesia are present)	0.264
LMC (Both Wearing OFF and levodopa-induced dyskinesia are absent)	-4.715
REM sleep behavior disorders	1.261
Number of support vectors: 34

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; REM: Rapid eye movement; PD: Parkinson's disease.