Fig. 3 | Scientific Reports

Fig. 3

From: Prostate cancer diagnosis using sensitive and sophisticated machine learning classifiers based on non-invasive urinary RNA biomarkers (PCASSO)

Fig. 3

Diagnostic performances of multiple urinary RNA biomarker-based ML analysis. (a, b) Predictor values calculated as decision probability for the test set using four different ML algorithms (RF, NB, GB, MLP). Predictor values using the 3-feature combination (a) and the 9-feature combination (b). The predictor value close to 1 indicates that the patient is likely a PCa patient, while the predictor value close to 0 indicates that the patient is likely a BPH patient. Open circles: predictor values for BPH patients; filled circles: predictor values for PCa patients. (c) Decision margin based on predictor values for individual patients. The decision margin has a value equal to the distance from the threshold (0.5), and the sign is positive when the diagnosis is correct and negative when the diagnosis is incorrect. In the box-and-whiskers plot, the ends of the box mark the first quartile (25%) and third quartile (75%). The two whiskers extend from the first quartile to the smallest value and from the third quartile to the largest value. The median is shown with a dashed line. The Kolmogorov–Smirnov test was used to compare the distribution of decision margin values for the 3- and 9-feature combinations (ns > 0.05, ** p = 0.001–0.01, *** p = 0.001–0.0001).

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