Advancing Breast Cancer Diagnosis: A Comprehensive Study of Machine Learning Algorithms on Histological Tumor Characteristics

Breast cancer is a prevalent and consequential form of cancer that impacts women globally, leading to a substantial number of newly diagnosed cases and fatalities. Its early detection is crucial for improved prognosis and survival rates, as it allows for timely medical intervention. Accurate differentiation between benign and malignant tumors also helps prevent unnecessary treatments. As a result, substantial research is dedicated to precise breast cancer diagnosis and categorization of patients. Machine learning (ML) methods are greatly appreciated in this domain because of their capacity to recognize essential characteristics within intricate datasets, rendering them the favored methodology for pattern classification and prediction in breast cancer studies. The main objective of this research is to investigate how ML techniques can be applied in the diagnosis and prognosis of breast cancer. It begins with an overview of ML techniques such as Linear Discriminant Analysis (LDA), Test Random Forest Classifier, and PCR. Afterward, the research examines how these techniques are applied in breast cancer studies, focusing on the utilization of the Wisconsin breast cancer database (WBCD) as the main data source for comparing outcomes obtained from various algorithms. Additionally, the study introduces a healthcare system model derived from recent research. The findings indicate that machine learning models offer specific contributions to breast cancer diagnosis. However, they also highlight the necessity for additional accuracy refinement.