Abstract

Cancer is the leading cause of death before the age of 70 years. An important step for reducing the cancer mortality can be its early detection. To improve the early diagnosis of cancer, we propose a novel probability calibration method based on the fuzzy set theory. Our approach was tested on the detection of female breast cancer and lung cancer. These are complicated by a small data set for the first case and by highly imbalanced data for the second case. In both cases, our probability calibration method improved the Log Loss metric (the best result was improved by 48.86%), the Brier score (the best result was improved by 13.24%), and the Precision-Recall metric (the best result was improved by 13.94%). The application field of our algorithm can be extended to any progressive diseases and events without a clearly defined boundary.

中文翻译：

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使用模糊集理论进行概率校准以改善早期癌症检测

摘要

癌症是70岁之前死亡的主要原因。降低癌症死亡率的一个重要步骤是早期发现。为了提高癌症的早期诊断，我们提出了一种基于模糊集理论的新颖的概率校准方法。我们的方法在女性乳腺癌和肺癌的检测中进行了测试。由于第一种情况的数据集较小和第二种情况的数据高度不平衡，这些情况变得复杂。在这两种情况下，我们的概率校准方法都改进了 Log Loss 指标（最佳结果提高了 48.86%）、Brier 分数（最佳结果提高了 13.24%）和 Precision-Recall 指标（最佳结果提高了 13.24%）。 13.94%）。我们算法的应用领域可以扩展到任何没有明确界限的进行性疾病和事件。