Machine learning allows robust classification of lung neoplasm tissue using an electronic biopsy through minimally-invasive electrical impedance spectroscopy.

New bronchoscopy techniques like radial probe endobronchial ultrasound have been developed for real-time sampling characterization, but their use is still limited. This study aims to use classification algorithms with minimally invasive electrical impedance spectroscopy to improve neoplastic lung tissue identification during biopsies. Decision Tree, Support Vector Machines (SVM), Ensemble Method, K-Nearest Neighbors, Naïve Bayes and Discriminant Analysis were applied using mean averaged bioimpedance modulus and phase angle spectra from lung tissue across 15 frequencies (15-307 kHz). Mann-Whitney U test assessed statistical significance between neoplasm and other tissues. Grid search analysis was conducted to determine the optimal hyperparameter configuration for each model, employing a 5-fold cross-validation approach. Model performance was evaluated using Receiver Operating Characteristic curves, with the Area Under Curve (AUC), precision, recall, and F1-score calculated. All the frequencies used to train and test the algorithms obtained high significant differences between neoplasm and the other types of tissues (P < 0.001). All the algorithms implemented obtained an accuracy, AUC and F1-score above the 95% except for Naïve Bayes. Decision Tree, Discriminant Analysis and SVM algorithms are suitable for the implementation of a new low-cost guidance method during bronchoscopy.