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* Corresponding author 1 SPMC - Signal Processing and Multimedia Communications research group

Abstract : Over the last decade many studies in the gynecology literature have been investigating the performance of diagnosis models such as Univariate, Risk of Malignancy Index RMI and Logistic Regression LR. Typical performance results are claimed in terms of sensitivity SEN, specificity SPE, accuracy ACC, Positive Predictive Value PPV, Negative Predictive Value NPV, with some studies als including Receiver Operating Characteristic ROC curve and its Area Under the Curve AUC. It remains, however, that all these measures do not reflect any sample size and thus making it sometimes difficult to assess with confidence the true performance of these diagnosis models, in particular for small sample size. In this paper, we propose to use systematically, a ROC-based methodology that makes possible to calculate the Confidence Interval CI at each ROC point. The methodology is generic and robust to sample size, and based on Probability Density Function PDF without any assumption on the distribution. We illustrate its use on 6 recent studies and show that results with the additional AUC 95% CI contour is more adequate to compare the performance of these diagnosis models, especially with studies using different sample size.

keyword : ROC AUC Confidence Intervals Diagnosis Models Gynecology





Autor: Brahim Hamadicharef -

Fuente: https://hal.archives-ouvertes.fr/



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