🤖 AI Summary
This study addresses the challenge of early cancer diagnosis by leveraging intrinsic differences in the electrical properties of healthy and malignant cells. The authors construct the first integrated dataset of multi-source bioelectrical parameters—including conductivity and permittivity—through a systematic review of 33 published studies. They employ three supervised machine learning algorithms—Random Forest, Support Vector Machine, and K-Nearest Neighbors—with hyperparameter optimization to enhance classification performance. The optimized Random Forest model, configured with 100 estimators and a maximum depth of 4, achieves an accuracy of 90%, while KNN and SVM attain F1 scores of 78% and 76.5%, respectively. This work represents the first systematic integration of diverse bioelectrical features with machine learning, offering a high-accuracy, label-free, and non-invasive approach for cancer screening.
📝 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 machine learning for improved diagnostic decision-making. Similarly, for KNN and SVM, the F1 score peaked at approximately 78% and 76.5%, respectively. Future work will explore incorporating additional discriminative features, leveraging stimulated datasets, and optimizing hyperparameter through advanced search strategies. Ultimately, hardware prototype with embedded micro-electrodes and real-time control systems could pave the path for practical diagnostic tools capable of in-situ cell classification.