Preoperative prediction of malignancy of ovarian tumours using modified sequential non-uniform procedure

In this paper, we present an extension of sequential non-uniform procedure (SNuP) with application of the method to ovarian tumour data, obtained during multicentre study by the International Ovarian Tumour Analysis Group (IOTA). The inference method combines feature selection based on the Kullback information gain and a step-wise classification procedure to produce a reliable, interpretable and robust model. In particular, we extend SNuP to enable it to handle continuous variables without the need for manual specification of thresholds. We applied the extended model to an ovarian tumour data set to distinguish between malignant and benign tumours. The performance of the model was assessed using ROC analysis and gave 86.9% of sensitivity and 84.3% of specificity with overall accuracy level of 84.9%.