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. 2018 Mar 7;19(3):756. doi: 10.3390/ijms19030756

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© 2018 by the authors.

Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

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Discrimination abilities of mathematical models. (a) Distribution of predicted probabilities of malignancy (M) calculated by multiple logistic regression (MLR) and (b) its ROC curve. (c) Distribution of predicted probability of the ADTree model and (d) its ROC curve. (e) The ADTree model to discriminate M (n = 201) from B + C (n = 31). Total scores <0 and >0 indicated the higher and lower probability of M. All values of the thresholds in this tree should be ×10⁻⁶ to calculate the probability of M. p-values of (a) and (c) were calculated using the Kruskal–Wallis test with Dunn’s post-test. **** p < 0.0001.

Discrimination abilities of mathematical models. (a) Distribution of predicted probabilities of malignancy (M) calculated by multiple logistic regression (MLR) and (b) its ROC curve. (c) Distribution of predicted probability of the ADTree model and (d) its ROC curve. (e) The ADTree model to discriminate M (n = 201) from B + C (n = 31). Total scores <0 and >0 indicated the higher and lower probability of M. All values of the thresholds in this tree should be ×10⁻⁶ to calculate the probability of M. p-values of (a) and (c) were calculated using the Kruskal–Wallis test with Dunn’s post-test. **** p < 0.0001.