Support vector machine with optimized parameters for the classification of patients with COVID-19

• Autores: Marín Rodríguez, William Joel, Andrade-Girón, Daniel, Carreño-Cisneros, Edgardo, Mejía-Dominguez, Cecilia, Velásquez-Gamarra, Julia, Villarreal-Torres, Henry, Meleán-Romero, Rosana
• Formato: artículo
• Fecha de Publicación: 2023
• Institución: Universidad Tecnológica del Perú
• Repositorio: UTP-Institucional
• Lenguaje: inglés
• OAI Identifier: oai:repositorio.utp.edu.pe:20.500.12867/7844
• Enlace del recurso: https://hdl.handle.net/20.500.12867/7844; https://doi.org/10.4108/eetpht.9.3472
• Nivel de acceso: acceso abierto
• Materia: Machine learning; Epidemiological models; COVID-19; https://purl.org/pe-repo/ocde/ford#3.00.00

Introduction: The COVID-19 pandemic has had a significant impact worldwide, especially in health, where it is crucial to identify patients at high risk of clinical deterioration early. Objective: This study aimed to design a model based on the support vector machine (SVM) algorithm, optimizing its parameters to classify patients with suspected COVID-19. Methodology: One thousand patient records from two health establishments in Peru were used. After applying data preprocessing and variable engineering, the sample was reduced to 700 records. The construction of the model followed a machine learning methodology, using the linear, polynomial, sigmoid, and radial kernel functions, along with their estimated optimal parameters, to ensure the best performance. Results: The results revealed that the SVM model with the linear and sigmoid kernels presented an accuracy of 95%, surpassing the polynomial kernel with 94% and the radial kernel (RBF) with 94%. In addition, a value of 0.92 was obtained for Cohen's kappa, which measures the degree of agreement between the predictions of the machine learning model and the actual results, which indicates an excellent deal for the linear and sigmoid kernel. Conclusions: In conclusion, the SVM model with linear and sigmoid kernels could be a valuable tool for identifying patients at high risk of clinical deterioration in the context of the COVID-19 pandemic.