Numerous researchers approved discrepancies in dielectric properties between malignant and normal tissues. Such discrepancies serve as a foundation for the development of computer-aided diagnostic technologies. In this study, machine learning methods were proposed for discrimination between normal and malignant colorectal tissues based on discrepancies in their dielectric properties. To do so, first, two independent-sample t -tests and receiver operating characteristic curve analysis were utilised to examine discrimination power with respect to three types of features, namely, permittivity, conductivity and Cole–Cole fitting parameters. K -nearest neighbour and support vector machine classifiers were used to assess the possibility of combining these features for better classification accuracy. Obtained k -fold cross-validation accuracy reached 88.2%. The obtained accuracy indicated the potential capability of discrimination between normal and malignant colorectal tissues based on discrepancies in their dielectric properties.

中文翻译：

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使用机器学习方法基于介电特性差异区分正常和恶性大肠组织

许多研究人员批准了恶性组织与正常组织之间介电特性的差异。这种差异为计算机辅助诊断技术的发展奠定了基础。在这项研究中，提出了机器学习方法，用于基于介电特性差异来区分正常和恶性大肠组织。为此，首先，两个独立样本Ť 测试和接收器工作特性曲线分析用于检查相对于三种类型特征的辨别力，即介电常数，电导率和科尔-科尔拟合参数。 ķ 最近邻和支持向量机分类器用于评估组合这些特征以实现更好的分类准确性的可能性。获得了ķ 折交叉验证的准确性达到88.2％。所获得的准确性表明，基于介电特性差异，可以区分正常和恶性大肠组织的潜在能力。