A data-driven pipeline reduces EIS measurements by 99% and achieves 80% accuracy with AUC 0.90 for healthy vs. cancer classification plus AUCs above 0.82 in multi-class oral lesion tasks using leave-one-patient-group-out validation.
Technology review: The use of electrical impedance scanning in the detection of breast cancer
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Optimizing In Vivo Oral Lesion Classification from Electrical Impedance Spectroscopy Using Data-driven Approaches
A data-driven pipeline reduces EIS measurements by 99% and achieves 80% accuracy with AUC 0.90 for healthy vs. cancer classification plus AUCs above 0.82 in multi-class oral lesion tasks using leave-one-patient-group-out validation.