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

Table 5.

Results for Rather absent Tinnitus Users, including standard deviation.

Rather absent Tinnitus Users
Accuracy F1-score AUC Precision Sensitivity Specificity
DT 0.565 (+/− 0.045) 0.573 (+/− 0.038) 0.518 (+/− 0.030) 0.702 (+/− 0.020) 0.643 (+/− 0.076) 0.389 (+/− 0.056)
RFC 0.599 (+/− 0.049) 0.593 (+/− 0.037) 0.548 (+/− 0.075) 0.704 (+/− 0.020) 0.723 (+/− 0.084) 0.319 (+/− 0.062)
SVM 0.566 (+/− 0.095) 0.578 (+/− 0.096) 0.603 (+/− 0.111) 0.765 (+/− 0.061) 0.531 (+/− 0.135) 0.646 (+/− 0.094)
CNB 0.531 (+/− 0.090) 0.541 (+/− 0.090) 0.586 (+/− 0.100) 0.741 (+/− 0.074) 0.494 (+/− 0.141) 0.614 (+/− 0.136)
KNC 0.644 (+/− 0.052) 0.607 (+/− 0.038) 0.586 (+/− 0.087) 0.703 (+/− 0.022) 0.839 (+/− 0.094) 0.205 (+/− 0.091)
LRC 0.548 (+/− 0.100) 0.558 (+/− 0.102) 0.583 (+/− 0.088) 0.733 (+/− 0.077) 0.534 (+/− 0.152) 0.578 (+/− 0.123)
MLP 0.663 (+/− 0.039) 0.606 (+/− 0.033) 0.609 (+/− 0.100) 0.702 (+/− 0.017) 0.891 (+/− 0.069) 0.149 (+/− 0.075)
XGB 0.598 (+/− 0.055) 0.599 (+/− 0.044) 0.573 (+/− 0.082) 0.716 (+/− 0.021) 0.693 (+/− 0.101) 0.384 (+/− 0.080)

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.