Comparison of Machine Learning Algorithms for Predicting Patients with Suspected COVID-19
DOI:
https://doi.org/10.56294/saludcyt2023336Keywords:
machine learning, classification, COVID-19, epidemic, morbidityAbstract
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
References
1. Altuna MA. Contra el diagnóstico. A propósito de las enseñanzas de Paul Feyerabend. Revista de treball social. 2017; p. 66-76.
2. Mojica Crespo R, Morales Crespo MM. Pandemia COVID-19, la nueva emergencia sanitaria de preocupación internacional: una revisión. Medicina de Familia. 2020; 46:65-77.
3. De León J, Cruz AP, Ramírez PA, Valencia YE, Carrillo CQ, Ayala EV. SARS-CoV-2 y sistema inmune: una batalla de titanes. Horizonte médico. 2020;20(2):5.
4. Martínez Chamorro E, Díez Tascón A, Ibañez Sanz L, Ossaba Vélez S, Borruel Nacenta S. Diagnóstico radiológico del paciente con COVID-19. Radiología (Madr., Ed. impr.). 2021.
5. Fuentes Marmolejo MD, Medina Parra WD. Diseño de un modelo predictivo-asistencial de pacientes infectados por Covid-19, mediante un modelo supervisado de Machine Learning basado en criterios de derivación hospitalaria o ambulatoria. 2021.
6. Algore M. Machine Learning With Python: The Definitive Tool to Improve Your Python Programming and Deep Learning to Take You to The Next Level of Coding and Algorithms Optimization.: Independently Published; 2021.
7. Rivera JR, del Pino Casado R. Manual práctico de enfermería comunitaria.: Elsevier.; 2020.
8. Lalueza A, Lora-Tamayo J, de la Calle C, Sayas-Catalán J, Arrieta E, Maestro G, et al. Utilidad de las escalas de sepsis para predecir el fallo respiratorio y la muerte en pacientes con COVID-19 fuera de Unidades de Cuidados Intensivos. Revista Clínica Española. 2015:s358-s363.
9. Sethi K, Gupta A, Gupta V, Jaiswal. Análisis comparativo de algoritmos de aprendizaje automático en diferentes conjuntos de datos. In Conferencia Internacional sobre Innovaciones en Computación. 2018; p. 87-91.
10. Gupta S, Saluja K, Goyal A, Vajpayee A, Tiwari V. Comparing the performance of machine learning algorithms using estimated accuracy. Measurement: Sensors. 2022;24(100432).
11. Osisanwo F, Akinsola J, Awodele O, Hinmikaipe J, Olakanmi J. Algoritmos de aprendizaje automático supervisados: Calsificación y comparación. Cómputo Tendencias Tecnol. 2017; 48(3):128-138.
12. Ahmad I, Basheri M, Iqbal M.: Performance Comparison of Support Vector Machine, Random Forest, and Extreme Learning Machine for Intrusion Detection. IEEE Acces. 2018; 6(1):33789–33795.
13. Géron A. Aprende machine learning con scikit-learn, keras y tensorflow. España: Anaya; 2020.
14. Zambrano J. ¿Aprendizaje supervisado o no supervisado?; 2018.
15. Witten IH, Frank E, Hall MA, Pal CJ, Data M. Practical Machine Learning Tools and Techniques, second edition San Francisco, CA, EEUU: Morgan Kaufmann; 2005.
16. Padilla-Ospina AM, Medina-Vásquez JE, Ospina-Holgin JH. Métodos de aprendizaje automático en los estudios prospectivos desde un ejemplo de la financiación de la innovación en Colombia. Rev.investig.desarro.innov. 2020;11(1):9-21.
17. Wiyono S, Abidin T. Estudio comparativo del aprendizaje automático KNN, SVM y algoritmo de árbol de decisión para predecir el rendimiento del estudiante. Revista Internacional de Investigación-Granthaalayah. 2019; 7(190-196).
18. Véliz Capuñay C. Aprendizaje Automático: Introducción al Aprendizaje Automático. Fondo Editorial PUCP. Lima-Perú: Fondo Editorial PUCP.; 2020.
19. Jeng JH, Hsieh JG, Nayvé Villavicencio C. Apoyo Vector Máquina Modelado para la predicción de COVID-19 basado en síntomas utilizando el lenguaje de programación R. ACM International Conference Proceeding Series. 2021;65 - 70.
20. Elguera Chavarría P, Prado Bush O, Barradas Ambriz J. Implementación de una escala de gravedad para la activación del equipo de respuesta rápida: NEWS 2. Med. 2019; 33(2):98–103
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Copyright (c) 2023 Daniel Andrade-Girón, Edgardo Carreño-Cisneros, Cecilia Mejía-Domínguez, William Marín-Rodriguez, Henry Villarreal-Torres (Author)
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