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. 2023 Jun 2;13:8989. doi: 10.1038/s41598-023-36172-7

Table 4.

Results for Non-permanent Tinnitus Users, including standard deviation.

Non-permanent Tinnitus Users
Accuracy F1-score AUC Precision Sensitivity Specificity
DT 0.598 (+/− 0.045) 0.611 (+/− 0.036) 0.522 (+/− 0.034) 0.753 (+/− 0.018) 0.681 (+/− 0.067) 0.359 (+/− 0.052)
RFC 0.641 (+/− 0.052) 0.636 (+/− 0.040) 0.554 (+/− 0.076) 0.752 (+/− 0.021) 0.769 (+/− 0.076) 0.273 (+/− 0.063)
SVM 0.554 (+/− 0.084) 0.576 (+/− 0.085) 0.578 (+/− 0.086) 0.789 (+/− 0.056) 0.539 (+/− 0.119) 0.595 (+/− 0.111)
CNB 0.519 (+/− 0.107) 0.538 (+/− 0.112) 0.580 (+/− 0.107) 0.777 (+/− 0.087) 0.484 (+/− 0.154) 0.622 (+/− 0.132)
KNC 0.702 (+/− 0.045) 0.641 (+/− 0.033) 0.576 (+/− 0.077) 0.743 (+/− 0.017) 0.914 (+/− 0.067) 0.093 (+/− 0.070)
LRC 0.529 (+/− 0.097) 0.550 (+/− 0.097) 0.572 (+/− 0.104) 0.779 (+/− 0.076) 0.505 (+/− 0.139) 0.597 (+/− 0.132)
MLP 0.720 (+/− 0.036) 0.648 (+/− 0.024) 0.601 (+/− 0.092) 0.746 (+/− 0.011) 0.943 (+/− 0.057) 0.078 (+/− 0.055)
XGB 0.632 (+/− 0.061) 0.635 (+/− 0.048) 0.576 (+/− 0.088) 0.761 (+/− 0.027) 0.734 (+/− 0.094) 0.339 (+/− 0.096)

Decision Tree (DT), Random Forest (RFC), Support Vector Machine (SVM), Complement Naive Bayes (CNB), k-nearest neighbors (KNC), Logistic Regression (LRC), Multi-layer Perceptron (MLP), Extreme Gradient Boosting (XGB)

Highest values are in bold.