Supervised Machine Learning Methods for Seasonal Influenza Diagnosis
Por:
Marquez E., Barrón-Palma E.V., Rodríguez K., Savage J., Sanchez-Sandoval A.L.
Publicada:
1 ene 2023
Categoría:
Clinical biochemistry
Resumen:
Influenza has been a stationary disease in Mexico since 2009, and this causes a high cost for the national public health system, including its detection using RT-qPCR tests, treatments, and absenteeism in the workplace. Despite influenza’s relevance, the main clinical features to detect the disease defined by international institutions like the World Health Organization (WHO) and the United States Centers for Disease Control and Prevention (CDC) do not follow the same pattern in all populations. The aim of this work is to find a machine learning method to facilitate decision making in the clinical differentiation between positive and negative influenza patients, based on their symptoms and demographic features. The research sample consisted of 15480 records, including clinical and demographic data of patients with a positive/negative RT-qPCR influenza tests, from 2010 to 2020 in the public healthcare institutions of Mexico City. The performance of the methods for classifying influenza cases were evaluated with indices like accuracy, specificity, sensitivity, precision, the f1-measure and the area under the curve (AUC). Results indicate that random forest and bagging classifiers were the best supervised methods; they showed promise in supporting clinical diagnosis, especially in places where performing molecular tests might be challenging or not feasible. © 2023 by the authors.
Filiaciones:
Marquez E.:
Genomic Medicine Department, General Hospital of México “Dr. Eduardo Liceaga”, Mexico City, 06726, Mexico
Barrón-Palma E.V.:
Genomic Medicine Department, General Hospital of México “Dr. Eduardo Liceaga”, Mexico City, 06726, Mexico
Rodríguez K.:
Institute for Research in Applied Mathematics and Systems, National Autonomous University of Mexico, Mexico City, 04510, Mexico
Savage J.:
Signal Processing Department, Engineering School, National Autonomous University of Mexico, Mexico City, 04510, Mexico
Sanchez-Sandoval A.L.:
Genomic Medicine Department, General Hospital of México “Dr. Eduardo Liceaga”, Mexico City, 06726, Mexico
Green Published, gold, All Open Access, Gold, Green
|