Machine learning in epidemiology
Characterization of risk factors related to the occurrence of pulmonary and extra pulmonary tuberculosis in the province of Settat
Keywords:
Tuberculose, pulmonaire, extra pulmonaire, Facteurs de risque, ProbitAbstract
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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Copyright (c) 2022 fatima ezzahra salamate, mohamed el azhari, jamal zahi
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