Detect the disrupted brain structural connectivity in type 2 diabetes mellitus patients without cognitive impairment

Table 2 Performance Evaluation for type 2 diabetes mellitus classification models

Models	Accuracy	Precision	Recall	F1-score
Connectivity baseline model	0.5011 (0.0064)	0.5009 (0.0085)	0.5333 (0.0195)	0.5104 (0.0105)
Local surface model	0.4678 (0.0056)	0.4840 (0.0103)	0.4733 (0.0161)	0.4649 (0.0056)
Local volume model	0.3614 (0.0057)	0.3478 (0.0111)	0.3844 (0.0274)	0.3610 (0.0179)
Local baseline model	0.3824 (0.0066)	0.3616 (0.0129)	0.4044 (0.0376)	0.3772 (0.0226)
Advanced connectivity models
Logistic	0.8099 (0.0071)	0.7983 (0.0072)	0.8311 0.0162)	0.8104 (0.0083)
Ridge (α = 0.9)	0.8304 (0.0058)	0.8694 (0.0066)	0.7867 (0.0189)	0.8185 (0.0089)
SVM	0.8093 (0.0026)	0.8127 (0.0035)	0.8088 (0.0106)	0.8065 (0.0038)
Connectivity interaction models
Logistic	0.8204 (0.0071)	0.8011 (0.0076)	0.8511 (0.0125)	0.8232 (0.0081)
Ridge (α = 0.2)	0.9251 (0.0007)	0.9377 (0.0026)	0.9155 (0.0018)	0.9249 (0.0006)
SVM	0.8204 (0.0049	0.8350 (0.0089)	0.8088 (0.00106)	0.8170 (0.0059)

SVM: Supporting vector machine; Logistic: Logistic classification; Ridge: Ridge classification (α is the hyper-parameter α tuning selected by the model). The process of the connection feature and interaction term selection, as well as the model evaluation metrics were included in the Supplementary material.