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Medical diagnosis and prognosis using machine learning methods is usually represented as a supervised classification problem, where a model is built to distinguish "normal" from "abnormal" cases. If cases are available from only one class, this approach is not feasible.To evaluate the performance of classification via outlier detection by one-class support vector machines (SVMs) as a means of identifying abnormal cases in the domain of melanoma prognosis.Empirical evaluation of one-class SVMs on a data set for predicting the presence or absence of metastases in melanoma patients, and comparison with regular SVMs and artificial neural networks.One-class SVMs achieve an area under the ROC curve (AUC) of 0.71; two-class algorithms achieve AUCs between 0.5 and 0.84, depending on the available number of cases from the minority class.One-class SVMs offer a viable alternative to two-class classification algorithms if class distribution is heavily imbalanced.

To improve the calibration of logistic regression (LR) estimates using local information.Individualized risk assessment tools are increasingly being utilized. External validation of these tools often reveals poor model calibration.We combine a clustering algorithm with an LR model to produce probability estimates that are close to the true probabilities for a particular case. The new method is compared to a standard LR model in terms of calibration, as measured by the sum of absolute differences (SAD) between model estimates and true probabilities, and discrimination, as measured by area under the ROC curve (AUC).We evaluate the new method on two synthetic data sets. SADs are significantly lower (p0.0001) in both data sets, and AUCs are significantly higher in one data set (p0.01).The results suggest that the proposed method may be useful to improve the calibration of LR models.