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A method to analysis Prostate Cancer stages and treatments

机译:分析前列腺癌分期和治疗方法

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C4.5 decision tree construction algorithm is widely used in medical field. But this algorithm C4.5 does not perform efficiently for mass calculations. Hence, in this paper an effort has been made to combine K-means and apriori algorithm results as the inputs of C4.5 algorithm to get better result. This technique has been applied in synthesized “Prostate Cancer Datasets” and try to achieve a robust and reliable classification to discover proper stages and treatments for the prostate cancer. We use WEKA data mining tool for our experiment purpose.
机译:C4.5决策树构造算法在医学领域得到了广泛的应用。但是,此算法C4.5不能有效地进行质量计算。因此,本文尝试将K均值和先验算法结果结合起来作为C4.5算法的输入,以获得更好的结果。该技术已应用于合成的“前列腺癌数据集”中,并试图实现可靠而可靠的分类,以发现前列腺癌的适当分期和治疗方法。我们将WEKA数据挖掘工具用于实验目的。

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