Support Vector Machine Classification - Breast Cancer Diagnosis Based on Electrical Impedance Characteristics of Breast Tissue

In this article, we discuss the application context of Support Vector Machine (SVM), a novel machine learning method. SVM is founded on Statistical Learning Theory (STL) proposed by Vapnik. STL adopts the Structural Risk Minimization (SRM) principle, which minimizes both sample point errors and structural risk, thereby improving the model's generalization capability. Importantly, SVM applications are not limited by data dimensionality. Specifically, for linear classification, the separating hyperplane is positioned to maximize the margin between two classes; for nonlinear classification, kernel methods transform nonlinear classification problems into linear separation tasks in high-dimensional spaces through feature mapping techniques.

Regarding key technologies, this chapter will detail SVM's classification principles and their application to breast cancer diagnosis based on electrical impedance spectrum characteristics of breast tissue. The breast is a vital organ in females, with numerous disease types and complex etiologies. Breast cancer, one such disease, has become a major malignant tumor threatening women's health. Recent years have shown a significant increase in breast cancer incidence, earning it the medical designation as "the number one killer of female health."

Research findings indicate that different biological tissues exhibit distinct electrical resistance characteristics under DC conditions. Biological tissue impedance shows significant variations with changing frequencies of applied electrical signals. Common electrical impedance measurement methods include Impedance Spectroscopy, Electrical Impedance Scanning (EIS), and Electrical Impedance Tomography (EIT). Impedance Spectroscopy measures tissue impedance variations across frequency spectra; EIS detects tissue distortions by comparing conductivity differences between cancerous, normal, and benign tissues; EIT uses peripheral electrode arrays with weak measurement currents to extract features and reconstruct cross-sectional impedance images.

Although current impedance measurements still show some deviations, studies confirm significant impedance characteristic differences between cancerous and normal tissues. Therefore, breast tissue impedance features can be applied to breast cancer examination and diagnosis. Given impedance measurement's advantages of being non-invasive, cost-effective, simple to operate, and widely acceptable to doctors and patients, measurement system accuracy continues improving with technological advancements. Breast cancer diagnosis technology based on breast tissue impedance characteristics will undoubtedly play a unique role in clinical examination and diagnosis.