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

Cyrille Agossou; Mintodê Nicodème Atchadé; Aliou Moussa Djibril; Svetlana Vladimirovna Kurisheva; Cyrille Agossou; Mintodê Nicodème Atchadé; Aliou Moussa Djibril; Svetlana Vladimirovna Kurisheva

doi:10.3934/mbe.2022080

Mathematical Biosciences and Engineering

2022, Volume 19, Issue 2: 1697-1720. doi: 10.3934/mbe.2022080

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Research article

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

1.
National Higher School of Mathematics Genius and Modelization, National University of Sciences, Technologies, Engineering and Mathematics, Abomey, Benin Republic
2.
University of Abomey-Calavi/ International Chair in Mathematical Physics and Applications (ICMPA : UNESCO-Chair), 072 BP 50 Cotonou, Benin Republic
3.
Saint-Petersburg State University of Economics, Department of Statistics and Econometrics, Russian Federation

Received: 28 July 2021 Accepted: 11 November 2021 Published: 15 December 2021

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.
- breast cancer,
- tumor classification,
- cardiotoxicity,
- machine learning,
- mathematical modeling,
- numerical simulations
Citation: Cyrille Agossou, Mintodê Nicodème Atchadé, Aliou Moussa Djibril, Svetlana Vladimirovna Kurisheva. Mathematical modeling and machine learning for public health decision-making: the case of breast cancer in Benin[J]. Mathematical Biosciences and Engineering, 2022, 19(2): 1697-1720. doi: 10.3934/mbe.2022080

Related Papers:

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.

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