Research Article

Forecasting Breast Cancer: A Study of Classifying Patients’ Post-Surgical Survival Rates with Breast Cancer


  • Md Nurul Raihen Visiting Assistant Professor, Department of Mathematics and Statistics, Stephen F. Austin State University, Nacogdoches, TX, USA
  • Sultana Akter MS Student in Statistics, Department of Statistics, Western Michigan University, Kalamazoo, MI, USA


Breast cancer is the most lethal form of cancer that can strike women anywhere in the world. The most complex and tough undertaking in order to lower the death rate is the process of predicting a patient's likelihood of survival following breast cancer surgery. Due to the fact that this survival prediction is linked to the life of a woman, effective algorithms are required for the purpose of making the prognosis. It is of the utmost importance to accurately predict the survival status of patients who will have breast cancer surgery since this shows whether or not doing surgery is the actual approach for the specific medical scenario. Given the gravity of the situation, it is impossible to overstate how important it is to investigate new and improved methods of prediction in order to guarantee an accurate assessment of the patient's chances of survival. In this paper, we collect data and examine some models based on the survival of patients who underwent breast cancer surgery. The goal of this research is to evaluate the forecasting performance of various classification models, including the Linear regression model, logistic regression analysis, LDA, QDA, KNN, ANN, and Decision Tree. The results of the experiment on this dataset demonstrate the better performance of the came up with ANN approach, with an accuracy of 82.98 percent.

Article information


Journal of Mathematics and Statistics Studies

Volume (Issue)

4 (2)





How to Cite

Raihen, M. N., & Akter, S. (2023). Forecasting Breast Cancer: A Study of Classifying Patients’ Post-Surgical Survival Rates with Breast Cancer. Journal of Mathematics and Statistics Studies, 4(2), 70–78.



Prediction, Breast cancer, Classification, Regression analysis, KNN, ANN, Machine learning models