Mathematical modeling and machine learning for public health decision-making: the case of breast cancer in Benin

Math Biosci Eng. 2022 Jan;19(2):1697-1720. doi: 10.3934/mbe.2022080. Epub 2021 Dec 15.

Abstract

Breast cancer is the most common type of cancer in women. Its mortality rate is high due to late detection and cardiotoxic effects of chemotherapy. In this work, we used the Support Vector Machine (SVM) method to classify tumors and proposed a new mathematical model of the patient dynamics of the breast cancer population. Numerical simulations were performed to study the behavior of the solutions around the equilibrium point. The findings revealed that the equilibrium point is stable regardless of the initial conditions. Moreover, this study will help public health decision-making as the results can be used to minimize the number of cardiotoxic patients and increase the number of recovered patients after chemotherapy.

Keywords: breast cancer; cardiotoxicity; machine learning; mathematical modeling; numerical simulations; tumor classification.

MeSH terms

  • Algorithms
  • Benin
  • Breast Neoplasms* / diagnosis
  • Breast Neoplasms* / drug therapy
  • Female
  • Humans
  • Machine Learning
  • Models, Theoretical
  • Public Health
  • Support Vector Machine