利用电阻抗特征和监督机器学习预测细胞恶性程度
Prediction of Cellular Malignancy Using Electrical Impedance Signatures and Supervised Machine Learning
AI总结：
研究通过整合20篇文献中的535组生物电参数数据，利用随机森林、SVM和KNN算法评估电阻抗特征在细胞恶性预测中的应用，并结合物理信息框架提升模型可解释性。
摘要：
细胞的生物电特性如相对介电常数、电导率和特征时间常数在健康与恶性细胞间存在显著差异。本研究系统回顾了20篇文献，收集了kHz-MHz频率范围内的535组定量生物电参数数据，并评估其在预测建模中的用途。三种监督机器学习算法——随机森林（RF）、支持向量机（SVM）和K-近邻（KNN）被实施并调优关键超参数以评估分类性能。第二阶段，引入物理信息框架，从测量参数中推导出额外的介电描述符，如虚部介电常数、损耗正切和电荷弛豫时间。随机森林基于特征重要性分析确定了影响分类过程的最判别介电参数。结果表明，介电损耗相关参数，特别是虚部介电常数和电导率，对细胞状态分类有显著贡献。尽管结合物理推导的特征提高了模型可解释性和减少了过拟合倾向，但整体分类准确率与使用主要介电描述符训练的模型相当。所提出的方法突显了物理信息机器学习在生物医学诊断中介电光谱数据分析的潜力。
英文摘要：
Bioelectrical properties of cells such as relative permittivity, conductivity, and characteristic time constants vary significantly between healthy and malignant cells across different frequencies. These distinctions provide a promising foundation for diagnostic and classification applications. This study systematically reviewed 20 scholarly articles to compile 535 datasets of quantitative bioelectric parameters in the kHz-MHz frequency range and evaluated their utility in predictive modeling. Three supervised machine learning algorithms- Random Forest (RF), Support Vector Machine (SVM), and K-Nearest Neighbor (KNN) were implemented and tuned using key hyperparameters to assess classification performance. In the second stage, a physics informed framework was incorporated to derive additional dielectric descriptors such as imaginary permittivity, loss tangent and charge relaxation time from the measured parameters. Random Forest based feature importance analysis was employed to identify the most discriminative dielectric parameters influencing the classification process. The results indicate that dielectric loss related parameters, particularly imaginary permittivity and conductivity, contribute significantly to the classification of cellular states. While the incorporation of physics-derived features improves model interpretability and reduces overfitting tendencies, the overall classification accuracy remains comparable to models trained using primary dielectric descriptors. The proposed approach highlights the potential of physics-informed machine learning for improving the analysis of dielectric spectroscopy data in the biomedical diagnostics.
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