Estimation of Impedance Features and Classification of Carcinoma Breast Cancer Using Optimization Techniques

Abstract

Breast cancer is the most prevalent form of cancer and the primary cause of cancer-related mortality among women globally. Breast cancer diagnosis involves multiple variables, making it a complex process. Therefore, the accurate estimation of features for diagnosing breast cancer is of great importance. The present study used a dataset of 21 patients with carcinoma breast cancer. Polynomial regression analysis was used to non-invasively estimate six impedance features for the diagnosis of breast cancer, including the phase angle at 500 KHz (PA500), impedance distance between spectral ends (DA), area normalized by DA (A/DA), maximum of the spectrum (Max IP), the distance between impedivity (ohm) at zero frequency and the real part of the maximum frequency point (DR), and length of the spectral curve (P). The results indicated that the polynomial degrees needed to estimate the PA500, DA, A/DA, Max IP, DR, and P features based on tumor size were 2, 2, 3, 3, 2, and 2, respectively. Additionally, we utilized a nonlinear constrained optimization (NCO) analysis to calculate the eight threshold levels for the classification of the impedance features. The deduction of eight classifications for each feature may also be an effective tool for decision-making in breast cancer. These findings may help oncologists to estimate the impedance features for breast cancer diagnosis non-invasively.

Keywords

• breast cancer
• optimization
• regression
• tumor size
• class of cancer
• impedance feature