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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...
In this paper, we present a sequential nonuniform procedure, an inference method which combines feature selection based on the Kullback information gain and a step-wise classification procedure to produce a reliable, interpretable, and robust model. We applied the model to an ovarian tumor data set to distinguish between malignant and benign tumors. The performance of the model was assessed using...
Ovarian masses are common and a good pre-surgical assessment of their nature is important for adequate treatment. Bayesian Multi-Layer Perceptrons (MLPs) using the evidence procedure were used to predict whether tumors are malignant or not. Automatic Relevance Determination (ARD) is used to select the most relevant of the 40+ available variables. Cross-validation is used to select an optimal combination...
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