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

Table 9.

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

Non-permanent Tinnitus Users down
Accuracy F1-score AUC Precision Sensitivity Specificity
DT 0.640 (+/− 0.009) 0.648 (+/− 0.012) 0.641 (+/− 0.012) 0.634 (+/− 0.009) 0.663 (+/− 0.023) 0.618 (+/− 0.020)
RFC 0.665 (+/− 0.011) 0.670 (+/− 0.012) 0.733 (+/− 0.013) 0.661 (+/− 0.011) 0.678 (+/− 0.017) 0.652 (+/− 0.015)
SVM 0.608 (+/− 0.010) 0.605 (+/− 0.009) 0.640 (+/− 0.012) 0.610 (+/− 0.012) 0.599 (+/− 0.008) 0.617 (+/− 0.017)
CNB 0.569 (+/− 0.012) 0.542 (+/− 0.011) 0.609 (+/− 0.014) 0.579 (+/− 0.014) 0.510 (+/− 0.010) 0.629 (+/− 0.018)
KNC 0.642 (+/− 0.011) 0.643 (+/− 0.010) 0.690 (+/− 0.011) 0.641 (+/− 0.013) 0.646 (+/− 0.013) 0.638 (+/− 0.020)
LRC 0.593 (+/− 0.010) 0.577 (+/− 0.010) 0.618 (+/− 0.013) 0.600 (+/− 0.011) 0.556 (+/− 0.011) 0.629 (+/− 0.014)
MLP 0.637 (+/− 0.008) 0.630 (+/− 0.013) 0.694 (+/− 0.010) 0.643 (+/− 0.015) 0.620 (+/− 0.033) 0.654 (+/− 0.038)
XGB 0.665 (+/− 0.012) 0.657 (+/− 0.015) 0.723 (+/− 0.012) 0.674 (+/− 0.012) 0.641 (+/− 0.022) 0.689 (+/− 0.015)

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.