Background: The dataset from genes used to predict hepatitis C virus outcome was evaluated in a previous study using a conventional statistical methodology. Objective: The aim of this study was to reanalyze this same dataset using the data mining approach in order to find models that improve the classification accuracy of the genes studied. Methods: We built predictive models using different subsets of factors, selected according to their importance in predicting patient classification. We then evaluated each independent model and also a combination of them, leading to a better predictive model. Results: Our data mining approach identified genetic patterns that escaped detection using conventional statistics. More specifically, the partial decision trees and ensemble models increased the classification accuracy of hepatitis C virus outcome compared with conventional methods. Conclusions: Data mining can be used more extensively in biomedicine, facilitating knowledge building and management of human diseases.

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中文翻译：

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丙型肝炎病毒感染结果的分类准确性：数据挖掘方法

背景：在先前的研究中，使用常规统计方法评估了用于预测丙型肝炎病毒结果的基因的数据集。目的：本研究的目的是使用数据挖掘方法重新分析相同的数据集，以找到提高所研究基因分类准确性的模型。方法：我们使用因素的不同子集构建了预测模型，并根据它们在预测患者分类中的重要性进行选择。然后，我们评估了每个独立模型以及它们的组合，从而得出了更好的预测模型。结果：我们的数据挖掘方法确定了使用常规统计数据可以逃脱检测的遗传模式。进一步来说，与传统方法相比，部分决策树和集成模型提高了丙型肝炎病毒结局分类的准确性。结论：数据挖掘可以在生物医学中得到更广泛的应用，从而促进人类疾病的知识建设和管理。

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