Machine learning to aid in the diagnosis of chronic obstructive pulmonary disease
DOI:
https://doi.org/10.59681/2175-4411.v16.iEspecial.2024.1249Keywords:
Pulmonary disease, chronic obstructive, Data mining, Knowledge discoveryAbstract
Objective: to identify risk factors for the Chronic obstructive pulmonary disease in the Brazilian population. Method: through a process for knowledge discovery, and machine learning models, identify risk factors for the disease in the Brazilian population, based on the 2019 National Health Survey. Results: the best learning model was achieved with the algorithm Random Forest presenting an F1 measure of 75% for the test set. Conclusions: based on the analysis of the level of importance of the main factors such as asthma, age at risk, previous smoking, body mass index, household risk, among others, the first four stood out as the main risk factors.
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