Prediction of Cellular Malignancy Using Electrical Impedance Signatures and Supervised Machine Learning

TL;DR

This study explores the use of electrical impedance signatures combined with supervised machine learning to distinguish between healthy and malignant cells, emphasizing physics-informed features for improved diagnostic accuracy.

Contribution

It introduces a physics-informed framework that enhances cell classification by deriving additional dielectric parameters and analyzing their importance with machine learning.

Findings

Dielectric loss parameters are highly discriminative for cell classification.

Physics-derived features improve model interpretability and reduce overfitting.

Classification accuracy is comparable when using primary or physics-informed dielectric descriptors.

Abstract

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.