An Early Diagnosis of Breast Cancer through Integrated Model of Random Forest and Catboost

Breast cancer is a significant and leading cause of cancer in women, its incidence witnessing a notable surge in recent times. Detecting this condition at its inception holds paramount importance. Breast cancer is caused by irregular cell division within the chest, as a result of which benign or malignant tumours form. Thus, timely detection of breast cancer holds immense significance, with the potential to save numerous lives through effective treatment. “ML algorithms” are shown to be useful in the early detection of cancer. This survey performed a thorough comparing of ensemble models for detecting breast cancer. The results reveal the promise of the ensemble model combining Random Forest and CatBoost in forecasting breast cancer. This advancement has the potential to contribute to early detection, ultimately resulting in improved treatment outcomes.