Breast Cancer Detection Using Different Classification and Ensemble Methods

Abstract

Breast cancer causes the cells in the breast to grow in an uncontrolled manner. Countless research has been conducted to enhance the timely diagnose of breast cancer, which expands the likelihood of survival and hence the survival rates. Mammography photographs were the focus of the majority of the investigation. Early detection of cancer is frequently delayed because symptoms are frequently not visible during the early stages of cancer. Mammography scans, on the other hand, can sometimes produce false positives, putting the patient health in danger. Different techniques, which are simpler to implement and perform better with different data sets, are needed to develop more reliable and safer forecasts. Healthcare data is huge and unstructured. Machine learning has long been the methodology of choice in breast cancer pattern classification and forecast modelling because of its distinct benefits based on feature identification from a comprehensive breast cancer dataset. The study evaluates and compares the results of seven different ML algorithms based on different performance metrics.