Table 3:
Open in new tab

Performance metrics for IDH prediction models.

Logistic regressionRFXGBoostDeep learning (1 timeframe)Deep learning (3 timeframes)
Accuracy0.93 (0.92–0.93)0.93 (0.93–0.94)0.93 (0.93–0.93)0.93 (0.93–0.94)0.93 (0.93–0.93)
Recall0.45 (0.41–0.48)0.43 (0.39–0.46)0.48 (0.44–0.51)0.42 (0.38–0.45)0.55 (0.52–0.58)
Precision0.39 (0.36–0.43)0.40 (0.37–0.44)0.41 (0.38–0.45)0.40 (0.37–0.44)0.41 (0.38–0.44)
Macro F1 score0.69 (0.68–0.71)0.69 (0.67–0.71)0.70 (0.69–0.72)0.69 (0.67–0.70)0.72 (0.70–0.73)
AUPRC0.35 (0.32–0.392)0.36 (0.33–0.40)0.41 (0.38–0.45)0.35 (0.32–0.39)0.44 (0.40–0.48)
AUROC0.85 (0.84–0.87)0.86 (0.85–0.87)0.87 (0.86–0.89)0.87 (0.85–0.88)0.90 (0.89–0.91)
Specificity0.96 (0.95–0.96)0.96 (0.96–0.96)0.96 (0.96–0.96)0.96 (0.96–0.96)0.95 (0.95–0.96)
NPV0.97 (0.96–0.97)0.97 (0.96–0.97)0.97 (0.96–0.97)0.96 (0.96–0.97)0.97 (0.97–0.97)
MCC0.38 (0.35–0.41)0.38 (0.35–0.41)0.41 (0.38–0.44)0.37 (0.34–0.40)0.44 (0.41–0.47)
Logistic regressionRFXGBoostDeep learning (1 timeframe)Deep learning (3 timeframes)
Accuracy0.93 (0.92–0.93)0.93 (0.93–0.94)0.93 (0.93–0.93)0.93 (0.93–0.94)0.93 (0.93–0.93)
Recall0.45 (0.41–0.48)0.43 (0.39–0.46)0.48 (0.44–0.51)0.42 (0.38–0.45)0.55 (0.52–0.58)
Precision0.39 (0.36–0.43)0.40 (0.37–0.44)0.41 (0.38–0.45)0.40 (0.37–0.44)0.41 (0.38–0.44)
Macro F1 score0.69 (0.68–0.71)0.69 (0.67–0.71)0.70 (0.69–0.72)0.69 (0.67–0.70)0.72 (0.70–0.73)
AUPRC0.35 (0.32–0.392)0.36 (0.33–0.40)0.41 (0.38–0.45)0.35 (0.32–0.39)0.44 (0.40–0.48)
AUROC0.85 (0.84–0.87)0.86 (0.85–0.87)0.87 (0.86–0.89)0.87 (0.85–0.88)0.90 (0.89–0.91)
Specificity0.96 (0.95–0.96)0.96 (0.96–0.96)0.96 (0.96–0.96)0.96 (0.96–0.96)0.95 (0.95–0.96)
NPV0.97 (0.96–0.97)0.97 (0.96–0.97)0.97 (0.96–0.97)0.96 (0.96–0.97)0.97 (0.97–0.97)
MCC0.38 (0.35–0.41)0.38 (0.35–0.41)0.41 (0.38–0.44)0.37 (0.34–0.40)0.44 (0.41–0.47)

macro F1 score, macro-averaged F1 score.

Table 3:
Open in new tab

Performance metrics for IDH prediction models.

Logistic regressionRFXGBoostDeep learning (1 timeframe)Deep learning (3 timeframes)
Accuracy0.93 (0.92–0.93)0.93 (0.93–0.94)0.93 (0.93–0.93)0.93 (0.93–0.94)0.93 (0.93–0.93)
Recall0.45 (0.41–0.48)0.43 (0.39–0.46)0.48 (0.44–0.51)0.42 (0.38–0.45)0.55 (0.52–0.58)
Precision0.39 (0.36–0.43)0.40 (0.37–0.44)0.41 (0.38–0.45)0.40 (0.37–0.44)0.41 (0.38–0.44)
Macro F1 score0.69 (0.68–0.71)0.69 (0.67–0.71)0.70 (0.69–0.72)0.69 (0.67–0.70)0.72 (0.70–0.73)
AUPRC0.35 (0.32–0.392)0.36 (0.33–0.40)0.41 (0.38–0.45)0.35 (0.32–0.39)0.44 (0.40–0.48)
AUROC0.85 (0.84–0.87)0.86 (0.85–0.87)0.87 (0.86–0.89)0.87 (0.85–0.88)0.90 (0.89–0.91)
Specificity0.96 (0.95–0.96)0.96 (0.96–0.96)0.96 (0.96–0.96)0.96 (0.96–0.96)0.95 (0.95–0.96)
NPV0.97 (0.96–0.97)0.97 (0.96–0.97)0.97 (0.96–0.97)0.96 (0.96–0.97)0.97 (0.97–0.97)
MCC0.38 (0.35–0.41)0.38 (0.35–0.41)0.41 (0.38–0.44)0.37 (0.34–0.40)0.44 (0.41–0.47)
Logistic regressionRFXGBoostDeep learning (1 timeframe)Deep learning (3 timeframes)
Accuracy0.93 (0.92–0.93)0.93 (0.93–0.94)0.93 (0.93–0.93)0.93 (0.93–0.94)0.93 (0.93–0.93)
Recall0.45 (0.41–0.48)0.43 (0.39–0.46)0.48 (0.44–0.51)0.42 (0.38–0.45)0.55 (0.52–0.58)
Precision0.39 (0.36–0.43)0.40 (0.37–0.44)0.41 (0.38–0.45)0.40 (0.37–0.44)0.41 (0.38–0.44)
Macro F1 score0.69 (0.68–0.71)0.69 (0.67–0.71)0.70 (0.69–0.72)0.69 (0.67–0.70)0.72 (0.70–0.73)
AUPRC0.35 (0.32–0.392)0.36 (0.33–0.40)0.41 (0.38–0.45)0.35 (0.32–0.39)0.44 (0.40–0.48)
AUROC0.85 (0.84–0.87)0.86 (0.85–0.87)0.87 (0.86–0.89)0.87 (0.85–0.88)0.90 (0.89–0.91)
Specificity0.96 (0.95–0.96)0.96 (0.96–0.96)0.96 (0.96–0.96)0.96 (0.96–0.96)0.95 (0.95–0.96)
NPV0.97 (0.96–0.97)0.97 (0.96–0.97)0.97 (0.96–0.97)0.96 (0.96–0.97)0.97 (0.97–0.97)
MCC0.38 (0.35–0.41)0.38 (0.35–0.41)0.41 (0.38–0.44)0.37 (0.34–0.40)0.44 (0.41–0.47)

macro F1 score, macro-averaged F1 score.

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