Skip to main navigation menu Skip to main content Skip to site footer
×
Español (España) | English
Editorial
Home
Indexing
Original

Comparison of Machine Learning Algorithms for Predicting Patients with Suspected COVID-19

By
Daniel Andrade-Girón ,
Daniel Andrade-Girón

Universidad Nacional José Faustino Sánchez Carrión. Huacho, Perú

Search this author on:

PubMed | Google Scholar
Edgardo Carreño-Cisneros ,
Edgardo Carreño-Cisneros

Universidad Nacional José Faustino Sánchez Carrión. Huacho, Perú

Search this author on:

PubMed | Google Scholar
Cecilia Mejía-Dominguez ,
Cecilia Mejía-Dominguez

Universidad Nacional José Faustino Sánchez Carrión. Huacho, Perú

Search this author on:

PubMed | Google Scholar
William Marín-Rodriguez ,
William Marín-Rodriguez

Universidad Nacional José Faustino Sánchez Carrión. Huacho, Perú

Search this author on:

PubMed | Google Scholar
Henry Villarreal-Torres ,
Henry Villarreal-Torres

Universidad San Pedro. Chimbote, Perú

Search this author on:

PubMed | Google Scholar

Abstract

The coronavirus disease (COVID-19) outbreak has infected millions of people, causing a high death rate worldwide. Patients suspected of having COVID-19 are transferred to different health facilities, which has caused a saturation in care, for which it is necessary to have a prediction model to classify patients at high risk of clinical deterioration. The objective of the research was to compare classification algorithms based on automatic learning machines, for the prediction of clinical diagnosis in patients with COVID-19. 1000 records of patients with suspected SARS-CoV-2 infection who were admitted by the emergency service in health establishments in Peru were collected. After pre-processing the data and engineering the attributes, a sample of 700 records was determined. Models were designed and algorithms were compared: Logistic Regression, Support Vector Machine, Nearest Neighbors, Decision Tree, Random Forest, and Navie Bayes. The evaluation of the results of each algorithm was carried out using Accuracy, precision, sensitivity and Chohen's Kappa to know the degree of agreement between the prediction by the learning machine and the results of reality, that is, to what extent both results agree in their measurement. The algorithm that presented the best results was the Support Vector Machine and Random Forest, which predicted the patients with an accuracy of 97%, and Cohen's Kappa of 0.95, with figures higher than the other models evaluated.

How to Cite

1.
Andrade-Girón D, Carreño-Cisneros E, Mejía-Dominguez C, Marín-Rodriguez W, Villarreal-Torres H. Comparison of Machine Learning Algorithms for Predicting Patients with Suspected COVID-19. Salud, Ciencia y Tecnología [Internet]. 2023 Mar. 23 [cited 2024 Apr. 22];3:336. Available from: https://revista.saludcyt.ar/ojs/index.php/sct/article/view/336

The article is distributed under the Creative Commons Attribution 4.0 License. Unless otherwise stated, associated published material is distributed under the same licence.

Article metrics

Google scholar: See link

Metrics

Metrics Loading ...

The statements, opinions and data contained in the journal are solely those of the individual authors and contributors and not of the publisher and the editor(s). We stay neutral with regard to jurisdictional claims in published maps and institutional affiliations.