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MIAT: A novel attribute selection approach to better predict upper gastrointestinal cancer

机译：MIAT：一种新颖的属性选择方法，可以更好地预测上消化道癌

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摘要

The use of data mining has led to many significant medical discoveries. However, many challenges still exist in using these methods for knowledge discovery within this field given that the large amounts of data medical practitioners collect often creates a curse of dimensionality. To address this challenge, attribute selection approaches have been developed. However, current approaches typically put equal weight on all values within that attribute. At times, and especially within medical domains, we claim that these approaches might miss attributes where only a small subset of attribute values contain a strong indication for one of the target values and thus should still be selected. To quantify this approach, we present MIAT, an algorithm that defines Minority Interesting Attribute Thresholds to find these important attribute values. As we developed MIAT to help better diagnose upper gastrointestinal cancer, we present how we use the attributes selected through this approach to build a predictive model for this cancer. To demonstrate MIAT's generality, we also applied it to a canonical Hungarian Heart Disease Dataset. In both datasets we found that MIAT yields significantly better accuracy and sensitivity over traditional attribute selection approaches.
机译：数据挖掘的使用导致了许多重要的医学发现。但是，鉴于医疗从业人员收集的大量数据通常会造成维数的诅咒，因此在这些领域中使用这些方法进行知识发现仍然存在许多挑战。为了应对这一挑战，已经开发了属性选择方法。但是，当前的方法通常对该属性内的所有值给予同等的权重。有时，尤其是在医学领域，我们认为这些方法可能会遗漏属性，其中只有一小部分属性值包含对目标值之一的强烈指示，因此仍应选择。为了量化这种方法，我们提出了MIAT，一种定义少数族裔有趣属性阈值以找到这些重要属性值的算法。随着我们开发MIAT帮助更好地诊断上消化道癌症，我们介绍了如何使用通过这种方法选择的属性来建立该癌症的预测模型。为了证明MIAT的普遍性，我们还将其应用于典型的匈牙利心脏病数据集。在这两个数据集中，我们发现MIAT的准确性和灵敏度明显优于传统的属性选择方法。

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