Research Article
Support vector machine with optimized parameters for the classification of patients with COVID-19
@ARTICLE{10.4108/eetpht.9.3472, author={Daniel Andrade-Gir\^{o}n and Edgardo Carre\`{o}o-Cisneros and Cecilia Mej\^{\i}a-Dominguez and Julia Vel\^{a}squez-Gamarra and William Mar\^{\i}n-Rodriguez and Henry Villarreal-Torres and Rosana Mele\^{a}n-Romero}, title={Support vector machine with optimized parameters for the classification of patients with COVID-19}, journal={EAI Endorsed Transactions on Pervasive Health and Technology}, volume={9}, number={1}, publisher={EAI}, journal_a={PHAT}, year={2023}, month={6}, keywords={machine learning, support vector machine, COVID-19, epidemic, morbidity}, doi={10.4108/eetpht.9.3472} }- Daniel Andrade-Girón
Edgardo Carreño-Cisneros
Cecilia Mejía-Dominguez
Julia Velásquez-Gamarra
William Marín-Rodriguez
Henry Villarreal-Torres
Rosana Meleán-Romero
Year: 2023
Support vector machine with optimized parameters for the classification of patients with COVID-19
PHAT
EAI
DOI: 10.4108/eetpht.9.3472
Abstract
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.
Copyright © 2023 Andrade-Girón et al., licensed to EAI. This is an open access article distributed under the terms of the CC BYNC-SA 4.0, which permits copying, redistributing, remixing, transformation, and building upon the material in any medium so long as the original work is properly cited.
