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COMPARISON OF THREE STATISTICAL CLASSIFIERS ON A PROSTATE CANCER DATA

机译：前列腺癌数据的三种统计分类的比较

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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.
机译：简介：检查了826位怀疑前列腺癌患者的数据集。寻找最佳的单一标志物和标志物组合，这些标志物可以在疾病的非常早期预测前列腺癌。方法：采用Logistic回归，多层感知器神经网络和k近邻法对标记物进行组合。在训练数据集上开发了每种方法的10个模型，并在测试数据集上验证了预测准确性。结果与结论：建立了模型的ROC，并估计了AUC。三种检查方法均给出了可比的结果。 AUC估计值的中位数为0.775，大于最佳单一标记的AUC。

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