Breast cancer disease prediction using ensemble techniques

Abstract

Breast Cancer is a highly lethal reproductive cancer that disproportionately affects women and is a leading cause of death worldwide. Cancer is characterized by the uncontrolled division and invasion of abnormal cells into the surrounding tissues. Early detection is crucial in the diagnosis of Breast Cancer, as it accounts for a significant percentage of cancer diagnoses and deaths among women. To prevent unnecessary tests, accurate classification of malignant and benign tumors is necessary. Researchers have developed numerous automated classification methods for Breast Cancer, with soft computing techniques being widely used due to their high performance in classification. Machine learning algorithms, known for their ability to identify critical features from medical datasets, are also extensively utilized in Breast Cancer prediction. Therefore, this study seeks to employ Boosting algorithms in machine learning to predict Breast Cancer accurately. Over the years, the mortality rate in Breast Cancer diagnosis has decreased due to research efforts.