Machine learning in epidemiology

Characterization of risk factors related to the occurrence of pulmonary and extra pulmonary tuberculosis in the province of Settat

Authors

  • Fatima Ezzahra SALAMATE University Hassan the 1st
  • Mohamed EL AZHARI University Hassan the 1st
  • Jamal Zahi University Hassan the 1st

Keywords:

Tuberculose, pulmonaire, extra pulmonaire, Facteurs de risque, Probit

Abstract

In this paper, we conduct a study based on machine learning tools to identify risk factors related to the occurrence of both forms of tuberculosis (noted "TB"). To do so, we use data collected from the registries of the Settat Center for Diagnosis of TB and Respiratory Diseases (CDTMR). As analysis method, we use the Probit logistic regression model. The results show that socio-demographic variables, such as patient age and gender, and clinical variables, such as registration group and duration of resistance, are risk factors that determine each of the forms of TB in patients in the province of Settat.

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Published

2022-08-21

How to Cite

SALAMATE, F. E., EL AZHARI, M., & Zahi, J. (2022). Machine learning in epidemiology: Characterization of risk factors related to the occurrence of pulmonary and extra pulmonary tuberculosis in the province of Settat. International Journal of Computer Engineering and Data Science (IJCEDS), 2(3). Retrieved from https://ijceds.com/ijceds/article/view/40