[2601.04478] Prediction of Cellular Malignancy Using Electrical Impedance Signatures and Supervised Machine Learning

Electrical Engineering and Systems Science > Signal Processing arXiv:2601.04478 (eess) [Submitted on 8 Jan 2026 (v1), last revised 5 Mar 2026 (this version, v3)] Title:Prediction of Cellular Malignancy Using Electrical Impedance Signatures and Supervised Machine Learning Authors:Shadeeb Hossain View a PDF of the paper titled Prediction of Cellular Malignancy Using Electrical Impedance Signatures and Supervised Machine Learning, by Shadeeb Hossain View PDF HTML (experimental) 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 33 scholarly articles to compile datasets of quantitative bioelectric parameters 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. Model effectiveness was evaluated using accuracy and F1 score as performance metrics. Results demonstrate that Random Forest achieved the highest predictive accuracy of ~ 90% when configured with a maximum depth of 4 and 100 estimators. These findings highlight the potential of integrating bioelectrical property analysis with ...