[Retracted] Implementing Critical Machine Learning (ML) Approaches for Generating Robust Discriminative Neuroimaging Representations Using Structural Equation Model (SEM)

Table 3

Correlation analysis.

Correlations
		Accuracy in critical ML [independent variable]	Age	Gender (, )	Experience in radiology	Years of training in radiology	Training provided to the ML algorithms (in years)	IQ
Pearson correlation	Accuracy in critical ML [independent variable]	1.000	.792	.219	.812	.646	.078	.569
	Age	.792	1.000	.358	.895	.826	.073	.576
	Gender (, )	.219	.358	1.000	.200	.364	-.110	.180
	Experience in radiology	.812	.895	.200	1.000	.667	-.051	.677
	Years of training in radiology	.646	.826	.364	.667	1.000	-.012	.323
	Training provided to the ML algorithms (in years)	.078	.073	-.110	-.051	-.012	1.000	-.064
	IQ	.569	.576	.180	.677	.323	-.064	1.000
Sig. (1-tailed)	Accuracy in critical ML (independent variable)	.	.000	.176	.000	.001	.371	.004
	Age	.000	.	.061	.000	.000	.380	.004
	Gender (, )	.176	.061	.	.199	.057	.322	.223
	Experience in radiology	.000	.000	.199	.	.001	.415	.001
	Years of training in radiology	.001	.000	.057	.001	.	.481	.082
	Training provided to the ML algorithms (in years)	.371	.380	.322	.415	.481	.	.395
	IQ	.004	.004	.223	.001	.082	.395	.