Cancer Detection in Highly Dense Breasts using Coherently Focused Time versa Microwave Imaging and Using Warm-Boot Random Forest Classifier

Breast cancer is one of the most common diseases among women all over the world. Many research can predict critical indicators, but these analyzes are also crucial using basic statistical methods. A prognostic model framework is used under the current system to call and predict MP4Ei for invasive disease-free survival breast cancer patients (IDFS). We need to use more effective, accurate, and scientific methods to guide the treatment method. But on the other hand, medical and omics with bed facilities will be introduced to improve the data model. In the proposed work, the speculation model was constructed with a Warm Boot - Random Forest to determine the significant prognostic factors for breast cancer incidence. The next set of data is based on the implementation of Warner Bros-Random Forest Breast Cancer Patients' Receptor Level Clusters Modeling Advanced Immunoassay Identification. Finally, the results were verified using tree construction and survival analysis. The proposed result obtained from high accuracy = 79.8% in low WB-random forest accuracy = 82.7% in high. This method's essential factors are cancer status, tumor size, discharge armpit lymph nodes, and positive lymph node as first stage treatment and diagnostic approach.