Comparison of three statistical classifiers on a prostate cancer data
Introduction: The dataset of 826 patients who were suspected of the prostate cancer was examined. The best single marker and the combination of markers which could predict the prostate cancer in very early stage of the disease were looked for. Methods: For combination of markers the logistic regression, the multilayer perceptron neural network and the k-nearest neighbour method were used. 10 models for each method were developed on the training data set and the predictive accuracy verified on the test data set. Results and conclusions: The ROCs for the models were constructed and AUCs were estimated. All three examined methods have given comparable results. The medians of estimates of AUCs were 0.775, which were larger than AUC of the best single marker.
prostate cancer prediction logistic regression neural networks k-nearest neighbour early detection cross-validation gower coefficient of similarity