Breast cancer (BC) biomarkers can radically improve the early detection in patients and, as a result, reduce mortality rate, whether for detecting individuals at increased risk of developing cancer or in the screening process. Finding a successful biomarker for breast cancer would be a fast and low-cost first solution to predicting BC, and it could potentially lead to a decline in the global BC mortality rate. However, biomarker exploration translates into the role of feature ranking and selection in machine learning terminology. This study explores the influence of using a particular biomarker or combinations of different biomarkers as predictors for breast cancer. Three different classification algorithms were integrated with a sequential backward selection model: support vector machine (SVM), random forests (RF), and Decision Trees (DTs). The result shows that the optimal set of biomarkers comprises Glucose, Resistin, homo, BMI, and Age using the SVM model. The sensitivity and specificity were 0.94 and 0.90, respectively and the 95% confidence interval for the AUC was [0.89, 0.98]. The result indicates that Glucose, Resistin, homo, BMI, and Age combined can serve as a crucial BC biomarker in BC screening and detection.

中文翻译：

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基于包装的特征选择方法，用于研究乳腺癌早期检测的潜在生物标志物

乳腺癌 (BC) 生物标志物可以从根本上改善患者的早期检测，从而降低死亡率，无论是用于检测患癌症风险较高的个体还是在筛查过程中。寻找成功的乳腺癌生物标志物将是预测 BC 的快速且低成本的首选解决方案，并且有可能导致全球 BC 死亡率下降。然而，生物标志物探索转化为机器学习术语中特征排序和选择的作用。本研究探讨了使用特定生物标志物或不同生物标志物组合作为乳腺癌预测因子的影响。三种不同的分类算法与顺序向后选择模型集成：支持向量机 (SVM)、随机森林 (RF) 和决策树 (DT)。结果表明，使用 SVM 模型，最佳生物标志物集包括葡萄糖、抵抗素、同型、BMI 和年龄。敏感性和特异性分别为 0.94 和 0.90，AUC 的 95% 置信区间为 [0.89, 0.98]。结果表明，葡萄糖、抵抗素、同型、BMI 和年龄组合可以作为 BC 筛查和检测的重要 BC 生物标志物。