Potentially modifiable predictors in models for undiagnosed and incident type 2 diabetes: A systematic review

Table 1 Summary of study design, modeling techniques, and validation strategies in type 2 diabetes mellitus models

Characteristic	Category		n	Percentage (%)
Overall	Studies included		82
Country/region	China		30	36.6
	Japan		12	14.6
	South Korea		4	4.9
	Iran		3	3.7
	Mexico		3	3.7
	United States		3	3.7
	Germany		2	2.4
	Qatar		2	2.4
	Singapore		2	2.4
	United Kingdom		2	2.4
	United Arab Emirates		2	2.4
	Other1		17	20.7
Study design	Prospective cohort		39	47.6
	Cross-sectional		20	24.4
	Retrospective cohort		14	17.1
	Mathematical modeling		2	2.4
	Case-control		2	2.4
	Population-based cohort		2	2.4
	Nested case-control		1	1.2
	Multi-cohort prospective		1	1.2
	Meta-analysis + prospective cohort validation		1	1.2
Modeling method	Traditional regression	Logistic regression	42	-2
		Cox proportional hazards model	32	-
	Regularized regression	LASSO/elastic net	17	-
	Machine learning	Support vector machine	5	-
		Decision tree	4	-
		Neural network	2	-
		Random forest	1	-
	Competing risk model	Fine–Gray model	2	-
	Other methods	Meta-analysis–based model	1	-
Model validation	Internal validation		67	-
		Bootstrap	21	-
		Cross-validation	13	-
		Split-sample	32	-
	External validation		28	-
	Both internal and external validation		15	-
	No validation		0	-

¹Other includes Denmark, Finland, Indonesia, Jordan, Malaysia, the Netherlands, Nigeria, Oman, Peru, Portugal, Russia, Saudi Arabia, Spain, Switzerland, Taiwan (China), 12 European countries, and the United Kingdom/Germany.

²Modeling methods and validation categories were not mutually exclusive because some studies applied more than one modeling or validation approach.

LASSO: Least absolute shrinkage and selection operator; SVM: Support vector machine.