Predicting Breast Cancer Using Gradient Boosting Machine

Breast Cancer is an uncontrolled growth in the breast. Breast cancer is a primary cause of death in women globally. The amount of death can be reduced by eliminating the accuracies in the diagnosis of the disease. The increase in accuracy of diagnosis can be increased through the predictive technology developed using the Gradient Boosting Machine. The prediction will improve the quality of the treatment process and the survivability rate of the patients. In this paper, we propose a system that will be used for predicting breast cancer via the use of classifiers and machine learning algorithm. The system is intended to be user- friendly and cost-effective to contribute to the fight against this deadly disease. The system will estimate the risk of prevalent breast cancer in the early stage of development. Ultimately, the results of the system will be compared with the pertinent medical results of each patient. The practical part has illustrated that GBM algorithm performed better than the other models. The GBM algorithm had better specificity, accuracy, and sensitivity.