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

Authors

  • Daniel Andrade-Girón Universidad Nacional José Faustino Sánchez Carrión image/svg+xml
  • Edgardo Carreño-Cisneros Universidad Nacional José Faustino Sánchez Carrión image/svg+xml
  • Cecilia Mejía-Dominguez Universidad Nacional José Faustino Sánchez Carrión image/svg+xml
  • Julia Velásquez-Gamarra Universidad Nacional José Faustino Sánchez Carrión image/svg+xml
  • William Marín-Rodriguez Universidad Nacional José Faustino Sánchez Carrión image/svg+xml
  • Henry Villarreal-Torres Universidad San Pedro image/svg+xml
  • Rosana Meleán-Romero Universidad César Vallejo image/svg+xml

DOI:

https://doi.org/10.4108/eetpht.9.3472

Keywords:

machine learning, support vector machine, COVID-19, epidemic, morbidity

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.

Downloads

Download data is not yet available.

References

Aftab, M., Amin, R., Koundal, D., Aldabbas, H., Alouffi, B., & Iqbal, Z. (2022). Classification of COVID-19 and Influenza Patients Using Deep Learning. Contrast Media Mol Imaging. doi:10.1155/2022/8549707 DOI: https://doi.org/10.1155/2022/8549707

Ahmad, I. B. (2018). Performance comparison of support vector machine, random forest, and extreme learning machine for intrusion detection. SPECIAL SECTION ON SURVIVABILITY STRATEGIES FOR EMERGING WIRELESS NETWORKS, 6(1). doi:10.1109/ACCESS.2018.2841987. DOI: https://doi.org/10.1109/ACCESS.2018.2841987

Aljameel, S. S., Khan, I. U., Aslam, N., Aljabri, M., & Alsulmi, E. S. (2021). Machine learning-based model to predict the disease severity and outcome in COVID-19 patients. Scientific programming, 1-10. doi:https://doi.org/10.1155/2021/5587188 DOI: https://doi.org/10.1155/2021/5587188

Altman, D. G. (2005). Diagnostic (STARD) and prognostic (REMARK) studies. Medicina clinica, 125, 49-55, doi:10.1016/s0025-7753(05)72210-7. Medicina clinica,, 49-55. doi:10.1016/s0025-7753(05)72210-7 DOI: https://doi.org/10.1016/S0025-7753(05)72210-7

Andrade Gontijo, M. C., Hamanaka, R. Y., & Ferreira de Araújo, R. (2022). Research data management: production and impact from Dimensions database data. Advanced Notes in Information Science, 2, 112–120. https://doi.org/10.47909/anis.978-9916-9760-3-6.89 DOI: https://doi.org/10.47909/anis.978-9916-9760-3-6.89

Aronszajn, N. (1944). La théorie générale des noyaux réproduisants et ses applications. . Proceedings of the Cambridge Philosophical Society,, 39, 133–153. doi: 10.1017/S0305004100017813. DOI: https://doi.org/10.1017/S0305004100017813

Balseca Valle, M., & Noroña Salcedo, D. (2022). Factores de riesgo e impacto psicológico en adolescentes de alta vulnerabilidad, durante confinamiento por COVID-19. Salud, Ciencia Y Tecnología, 2, 135. https://doi.org/10.56294/saludcyt2022135 DOI: https://doi.org/10.56294/saludcyt2022135

Betancourt, G. A. (2005). Las máquinas de soporte vectorial (SVMs). Scientia et technica., 1(27), 22.

Borges, S. E. N. (2023). Produção de materiais educativos sobre a covid-19 e a Hanseníase para os povos indígenas Guajajara e Canela na Amazonia Legal Maranhense, Brasil. Salud, Ciencia Y Tecnología - Serie De Conferencias, 2(1), 116. https://doi.org/10.56294/sctconf2023116 DOI: https://doi.org/10.56294/sctconf2023116

Campo León, E. (2017). Introducción a las máquinas de vector soporte (SVM) en aprendizaje supervisado. Trabajo de Fin de Grado en Matemáticas, Universidad de Zaragoza. Obtenido de https://zaguan.unizar.es/record/59156/files/TAZ-TFG-2016-2057.pdf

Campos Sánchez, C. M., Guillén León, L. A., Acosta Yanes, R. C., & Gil Oloriz, M. A. (2022). Metaverse: the future of medicine in a virtual world. Metaverse Basic and Applied Research, 1, 4. https://doi.org/10.56294/mr20224 DOI: https://doi.org/10.56294/mr20224

Castellanos, S., & Figueroa, C. (2023). Cognitive accessibility in health care institutions. Pilot study and instrument proposal. Data & Metadata, 2, 22. https://doi.org/10.56294/dm202322 DOI: https://doi.org/10.56294/dm202322

Chávarry-Ysla, P. del R., Piscoya Angeles, P. N., Castagnola-Sánchez, C. G., Oliva-Yarlaqué, Y. M., Montalvo Chacón, N., Dubo Araya, P. A., & Campillay Campillay, M. . (2023). Métodos de gestión y duelo disfuncional ante el COVID-19. Salud, Ciencia Y Tecnología, 3, 345. https://doi.org/10.56294/saludcyt2023345 DOI: https://doi.org/10.56294/saludcyt2023345

Cortés, M. E. (2021). La pandemia de COVID-19: importancia de estar alerta ante las zoonosis. Revista de la Facultad de Medicina Humana, 21(1), 151-156. doi:http://dx.doi.org/10.25176/rfmh.v21i1.3451 DOI: https://doi.org/10.25176/RFMH.v21i1.3451

Cura-González, I., Polentinos-Castro, E., Fontán-Vela, M., López-Rodríguez, J. A., & Martín-Fernández, J. (2022). ¿Qué hemos dejado de atender por la COVID-19? Diagnósticos perdidos y seguimientos demorados. Informe SESPAS 2022. Gaceta Sanitaria, 36(1), s36-s43. Obtenido de https://doi.org/10.1016/j.gaceta.2022.03.003 DOI: https://doi.org/10.1016/j.gaceta.2022.03.003

De León, J., Cruz, A. P., Ramírez, P. A., Valencia, Y. E., Carrillo, C. Q., & Ayala, E. V. (2020). SARS-CoV-2 y sistema inmune: una batalla de titanes. Horizonte médico, 20(2), 5. doi: https://doi.org/10.24265/horizmed.2020.v20n2.12 DOI: https://doi.org/10.24265/horizmed.2020.v20n2.12

Díaz-Chieng, L. Y., Auza-Santiváñez, J. C., & Robaina Castillo, J. I. (2022). The future of health in the metaverse. Metaverse Basic and Applied Research, 1, 1. https://doi.org/10.56294/mr20221 DOI: https://doi.org/10.56294/mr20221

Dilmi, S. (2022). Automatic COVID-19 diagnosis using deep learning features and support vector machines based on Bayesian optimization. ICAASE 2022 - 5th Edition of the International Conference on Advanced Aspects of Software Engineering, Proceedings. doi:10.1109/ICAASE56196.2022.9931584 DOI: https://doi.org/10.1109/ICAASE56196.2022.9931584

Dinar, A. M., Raheem, E. A., Abdulkareem, K. H., Mohammed, M. A., Oleiwie, M. G., Zayr, F. H., . . . Al-Andoli, N. M. (2022). Towards Automated Multiclass Severity Prediction Approach for COVID-19 Infections Based on Combinations of Clinical Data. Mobile Information Systems. doi: https://doi.org/10.1155/2022/7675925 DOI: https://doi.org/10.1155/2022/7675925

Elguera Chavarria, P., Prado Bush, O., & Barradas Ambriz, J. (2019). Implementación de una escala de gravedad para la activación del equipo de respuesta rápida: NEWS 2. Med Crit.33(2):98–103, url: http://www.scielo.org.mx/scielo.php?script=sci_arttext&pid=S2448-8. NEWS 2. Med, 33(2), 98–103. Obtenido de http://www.scielo.org.mx/scielo.php?script=sci_arttext&pid=S2448-8 DOI: https://doi.org/10.35366/87296

Fuentes Marmolejo, M. D., & Medina Parra, W. D. (2021). 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. Universidad de Guayaquil.

Garcia_Lamberechts, E. J., Martín-Sánchez, F. J., Julián-Jiménez, A., Llopis, F., Martinez-Ortiz, M., Arranz-Nieto, M., . . . González-Del Castillo. (2018). Modelo de riesgo a 30 días en los pacientes ancianos con infección y sindrome de respuestas imflamatoria sistemática atendidos en los servicios de urgencias. Emergencias (Sant Vicente del Horts, 241-246.

Géron, A. (2020). . Aprende machine learning con scikit-learn, keras y tensorflow. España: Anaya.

Gómez-Gómez A, G.-E. M.-R. (2020). Diagnóstico y tratamiento temprano de neumonía ante la pandemia por COVID-19 en San Luis Potosí. ¿Es posible implementar una estrategia para lograrlo? NCT Neumol y Cirugía Tórax, 79(4), 214-220. doi:10.35366/97962 DOI: https://doi.org/10.35366/97962

Granados, M. E. (2019). Máquinas de soporte vectorial y árboles de clasificación para la detección de operaciones sospechosas de lavado de activos. Lámpsakos, 21, 26-38,. doi:doi: https://doi.org/10.21501/21454086.2904 DOI: https://doi.org/10.21501/21454086.2904

Guhathakurata, S., Kundu, S., Chakraborty, A., & Banerjee, J. S. (2021). A novel approach to predict COVID-19 using support vector machine. Data Science for COVID-19, 351 - 364. doi:10.1016/B978-0-12-824536-1.00014- DOI: https://doi.org/10.1016/B978-0-12-824536-1.00014-9

Gupta, B. M., Kappi, M., Walke, R., & Bansal, M. (2023b). Covid-19 research in Bangladesh: A scientometric analysis during 2020-23. Iberoamerican Journal of Science Measurement and Communication, 3(1). https://doi.org/10.47909/ijsmc.445 DOI: https://doi.org/10.47909/ijsmc.445

Gupta, B., Kappi, M. M., Walke, R., Bansal, M., & Mandal, A. (2023a). Covid-19 associated coagulopathy (CAC): Global research output, 2020-2022 . Iberoamerican Journal of Science Measurement and Communication, 3(2). https://doi.org/10.47909/ijsmc.48 DOI: https://doi.org/10.47909/ijsmc.48

Gutiérrez Solano, J. G., & Solorzano Bernita, R. E. (2022). Complicaciones neuropsiquiátricas por COVID-19. Salud, Ciencia Y Tecnología, 2(S1), 223. https://doi.org/10.56294/saludcyt2022223 DOI: https://doi.org/10.56294/saludcyt2022223

Gutiérrez, E., Meller, L., Virdis, J. M., De Simón, F., Gurovich, C., & Fernández Leyes, L. (2020). Retweet or reply? Covid-19 and Twitter. The case of the city of Bahía Blanca (Argentina). AWARI, 1(2), e18. https://doi.org/10.47909/awari.79 DOI: https://doi.org/10.47909/awari.79

Jain, D., Shankar, V. G., & Devi, B. (2020). A Robust Approach of COVID-19 Indian Data Analysis Using Support Vector Machine. Lecture Notes in Electrical Engineering, 837, 355 - 366. doi:10.1007/978-981-16-8546-0_29 DOI: https://doi.org/10.1007/978-981-16-8546-0_29

Jeng, J.-H., Hsieh, J.-G., & Nayvé Villavicencio, C. (2021). Apoyo Vector MáquinaModelado para la predicción de COVID-19 basado en síntomas utilizando el lenguaje de programación R. ACM International Conference Proceeding Series, 65 - 70. doi:10.1145/3490725.3490735 DOI: https://doi.org/10.1145/3490725.3490735

Kalezhi, J., Chibuluma, M., Chembe, C., Chama, V., Lungo, F., & Kunda, D. (2022). Modelling Covid-19 infections in Zambia using data mining techniques. Results in Engineering, 13(100363). doi:https://doi.org/10.1016/j.rineng.2022.100363 DOI: https://doi.org/10.1016/j.rineng.2022.100363

Kavzoglu, T., & Colkesen, I. (2009). A kernel functions analysis for support vector machines for land cover classification. International Journal of Applied Earth Observation and Geoinformation, 11(5), 352-359. Obtenido de https://doi.org/10.1016/j.jag.2009.06.002 DOI: https://doi.org/10.1016/j.jag.2009.06.002

Kesav, N., & Jubukumar, M. G. (2022). A deep learning approach with Bayesian optimized Kernel support vector machine for Covid-19 diagnosis. Computer Methods in Biomechanics and Biomedical Engineering: Imaging and Visualization. doi:10.1080/21681163.2022.2099299 DOI: https://doi.org/10.1080/21681163.2022.2099299

Lalueza, A., Lora-Tamayo, J., de la Calle, C., Sayas-Catalán, J., Arrieta, E., Maestro, G., . . . Lumbreras, C. (2015). 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 Clinica Española, s358-s363. doi:10.1016/j.rce.2020.10.004 DOI: https://doi.org/10.1016/j.rce.2020.10.004

Lasluisa-Toalombo, P. M., Rosero Freire, D. A., Jacome-Lara, A. C., & Salazar-Garcés, L. F. (2022). Serological markers associated with poor prognosis in positive Covid-19 patients. Salud, Ciencia Y Tecnología, 2, 141. https://doi.org/10.56294/saludcyt2022141 DOI: https://doi.org/10.56294/saludcyt2022141

Li, Z., Wang, L., Huang, L. S., Zhang, M., Cai, X., Xu, F., . . . Liu, G. (2021). Efficient management strategy of COVID-19 patients based on cluster analysis and clinical decision tree classification. Scientific reports, 9626. doi:https://doi.org/10.1038/s41598-021-89187-3 DOI: https://doi.org/10.1038/s41598-021-89187-3

Llerena, C. A., & Aucanshala Shigla, E. V. (2023). Efectos adversos post vacunación contra el COVID-19 en adolescentes. Salud, Ciencia Y Tecnología, 3, 372. https://doi.org/10.56294/saludcyt2023372 DOI: https://doi.org/10.56294/saludcyt2023372

Macea-Anaya, M., Baena-Navarro, R., Carriazo-Regino, Y., Alvarez-Castillo, J., & Contreras-Florez, J. (2023). Designing a Framework for the Appropriation of Information Technologies in University Teachers: A Four-Phase Approach. Data & Metadata, 2, 53. https://doi.org/10.56294/dm202353 DOI: https://doi.org/10.56294/dm202353

Marinho de Sousa, R. P., & Shintaku, M. (2022). Data privacy policy: relevant observations for its implementation. Advanced Notes in Information Science, 2, 82–91. https://doi.org/10.47909/anis.978-9916-9760-3-6.112 DOI: https://doi.org/10.47909/anis.978-9916-9760-3-6.112

Martínez Chamorro, E., Díez Tascón, A., Ibañez Sanz, L., Ossaba Vélez, S., & Borruel Nacenta, S. (2021). Diagnóstico radiológico del paciente con COVID-19. Radiología (Madr., Ed. impr.). doi:https://doi.org/10.1016/j.rx.2020.11.001. DOI: https://doi.org/10.1016/j.rx.2020.11.001

Martín-Rodríguez, F. e. (2020). "¿Puede la Puntuación 2 de alerta temprana nacional prehospitalaria identificar a los pacientes en riesgo de mortalidad temprana intrahospitalaria? Un estudio de cohorte prospectivo multicéntrico". Corazón y pulmón. 585-591.

Mendoza-Ticona, A., Valencia, M. G., Quintana, A. A., Cerpa, C. B., Garcia, L. G., Álvarez, C. C., & Rivero, V. J. (2020). Clasificación clínica y tratamiento temprano de la COVID-19. Reporte de casos del Hospital de Emergencias Villa El Salvador, Lima-Perú. Acta Médica Peruana, 37(2). Obtenido de http://dx.doi.org/10.35663/amp.2020.372.968 DOI: https://doi.org/10.35663/amp.2020.372.968

Mesa Trujillo D, Zayas Argos CC, Verona Izquierdo AI, García Mesa I, López Zamora A. (2023). Caracterización de la capacidad funcional en Adultos Mayores. Interdisciplinary Rehabilitation / Rehabilitación Interdisciplinaria, 1.

Mohammad, M. A., Aljabri, M., Aboulnour, M., Mirza, S., & Alshobaiki, A. (2022). Classifying the mortality of people with underlying health conditions affected by COVID-19 using machine learning techniques. Applied Computational Intelligence and Soft Computing. doi: https://doi.org/10.1155/2022/3783058 DOI: https://doi.org/10.1155/2022/3783058

Mojica Crespo, R., & Morales Crespo, M. M. (2020). Pandemia COVID-19, la nueva emergencia sanitaria de preocupación internacional: una revisión. Medicina de Familia., 46, 65-77. doi:https://doi.org/10.1016/j.semerg.2020.05.010. DOI: https://doi.org/10.1016/j.semerg.2020.05.010

Mosquera, R., Castrillón , O., & Parra, L. (2018). Máquinas de Soporte Vectorial, Clasificador Naïve Bayes y Algoritmos Genéticos para la Predicción de Riesgos Psicosociales en Docentes de Colegios Públicos Colombianos. Inf Tecnol, 29(6), 153–62. doi:10.4067/S0718-07642018000600153 DOI: https://doi.org/10.4067/S0718-07642018000600153

Moyano, L. G., Millán, D., Curiale, A., Moyano, N. A., & Portillo, J. M. (2018). Clinical risk factors data classification with machine learning methos. CLIAP-San Rafael Mendoza Argentina.

Oliva, M., Silva Sandes, E., & Romero, S. (2022). Application of social network analysis to the institutional relations of the Higher Education System in the Rivera region-Livramento. AWARI, 3. https://doi.org/10.47909/awari.157 DOI: https://doi.org/10.47909/awari.157

Olusegun Oyetola, S., Oladokun, B. D., Ezinne Maxwell, C., & Obotu Akor, S. (2023). Artificial intelligence in the library: Gauging the potential application and implications for contemporary library services in Nigeria. Data & Metadata, 2, 36. https://doi.org/10.56294/dm202336 DOI: https://doi.org/10.56294/dm202336

Pérez Martínez, N. G., Vega Esparza, R. M., & López López, Y. Y. G. (2022). Analysis of organizational resilience in times of Covid-19: A bibliometric overview. Iberoamerican Journal of Science Measurement and Communication, 2(2). https://doi.org/10.47909/ijsmc.167 DOI: https://doi.org/10.47909/ijsmc.167

Pisner, D. A., & Schnyer, D. M. (2020). Support vector machine. In Machine learning. Academic Press. Obtenido de https://doi.org/10.1016/B978-0-12-815739-8.00006-7 DOI: https://doi.org/10.1016/B978-0-12-815739-8.00006-7

Ramírez, M. E., Ron, M., Mago, G., Hernandez–Runque, E., Martínez, M. D. C., & Escalona, E. (2023). Proposal for an epidemiological surveillance program for the prevention of occupational accidents and diseases in workers exposed to carbon dioxide (CO2) at a Venezuelan brewing company. Data & Metadata, 2, 55. https://doi.org/10.56294/dm202355 DOI: https://doi.org/10.56294/dm202355

Ramírez Moncada, J. A., Rodríguez Torres, E., & Zamora Reyes, J. R. (2023). Estrategias recreativas para suplir las carencias de niños y jóvenes en situaciones de la Covid-19 en el municipio Morón (Cuba). Región Científica, 2(1), 202328. https://doi.org/10.58763/rc202328 DOI: https://doi.org/10.58763/rc202328

Reyes Nuñez, M. A., & Simón Dominguez, J. I. (2020). Cómo estimar la letalidad del COVID-19. Revista Mexicana de Patología Clínica y Medicina de Laboratorio, 67(1), 4-8. doi: 10.35366/93845. DOI: https://doi.org/10.35366/93845

Rincon Soto, I. B., & Sanchez Leon, N. S. (2022). How artificial intelligence will shape the future of metaverse. A qualitative perspective. Metaverse Basic and Applied Research, 1, 12. https://doi.org/10.56294/mr202212 DOI: https://doi.org/10.56294/mr202212

Rios, J., Ulloa, G., & Borello Gianni, A. (2019). Aplicación de regresión con vectores de soporte en un sistema recomendador de actividades sociales. XXV Congreso Argentino de Ciencias de la Computación (CACIC)(Universidad Nacional de Río Cuarto, Córdoba. Códoba.

Rivera, J. R., & del Pino Casado, R. (2020). Manual práctico de enfermería comunitaria. Elsevier.

Rizwan, M. F., Farhad, R., & Imam, M. H. (2021). Support vector machine based stress detection system to manage COVID-19 pandemic related stress from ECG signal. AIUB Journal of Science and Engineering, 20(1), 8 - 16. doi:10.53799/AJSE.V20I1.112 DOI: https://doi.org/10.53799/ajse.v20i1.112

Rodríguez Yago, M., Alcalde, I., Gómez, R., Parias, M. N., Pérez, A., Canals, M., . . . Hernández-Tejedor, A. (2020). Recomendaciones sobre reanimación cardiopulmonar en pacientes con sospecha o infección confirmada por SARS_CoV-2 (COVID-19). Medicina Intensiva, 44(9), 566-576. doi:10.1016/j.medin.2020.05.004 DOI: https://doi.org/10.1016/j.medin.2020.05.004

Romero Hernández, S., Saavedra Uribe, J., Zamarrón López, E., Pérez Nieto, O., Figueroa Uribe, A., Guerrero Gutierrez , M., & Lopez Ferminde, J. (2020). Protocolo de atención para COVID-19 (SARS-CoV-2) de la Sociedad Mexicana de Medicina de Emergencias. Sociedad Mexicana de Medicina de Emergencias.

Sánchez Gómez, C. (2019). Desarrollo de soluciones software mediante aprendizaje automático en el ámbito de la salud. situación tecnológica y perspectivas. Obtenido de http://hdl.handle.net/10317/8110

Santos Amaral, L., Medeiros de Araújo, G., & Reinaldo de Moraes, R. A. (2022). Analysis of the factors that influence the performance of an energy demand forecasting model. Advanced Notes in Information Science, 2, 92–102. https://doi.org/10.47909/anis.978-9916-9760-3-6.111 DOI: https://doi.org/10.47909/anis.978-9916-9760-3-6.111

Santos Rocha, E., & Ferreira Araújo, R. (2022). Rapid scientific communication in times of pandemic: the attention of pre-prints online about Covid-19. Advanced Notes in Information Science, 2, 103–111. https://doi.org/10.47909/anis.978-9916-9760-3-6.114 DOI: https://doi.org/10.47909/anis.978-9916-9760-3-6.114

Sebo, T. A. R., Oentarto, A. S. A., & Situmorang, D. D. B. (2023). “Counseling-Verse”: A Survey of Young Adults from Faith-Based Educational Institution on the Implementation of Future Mental Health Services in the Metaverse. Metaverse Basic and Applied Research, 2, 42. https://doi.org/10.56294/mr202342 DOI: https://doi.org/10.56294/mr202342

Simhan, L., & Basupi, G. (2023). None Deep Learning Based Analysis of Student Aptitude for Programming at College Freshman Level. Data & Metadata, 2, 38. https://doi.org/10.56294/dm202338 DOI: https://doi.org/10.56294/dm202338

Singh, V., Poonia, R. C., Kumar, S., Dass, P., Agarwal, P., Agarwal, P., & Raja, L. (2020). Prediction of COVID-19 corona virus pandemic based on time series data using support vector machine. Journal of Discrete Mathematical Sciences and Cryptography, 23(8), 1583 - 1597. doi:10.1080/09720529.2020.1784535 DOI: https://doi.org/10.1080/09720529.2020.1784535

Specht González, A., Casas Núñez, Y. E., Meriño Pompa, Y., Naranjo Vázquez, S. Y., & Johnson Quiñones, M. (2022). Caracterización epidemiológica y complicaciones obstétricas de gestantes con diagnóstico de COVID-19. Salud, Ciencia Y Tecnología - Serie De Conferencias, 1(1), 6. https://doi.org/10.56294/saludcyt20226 DOI: https://doi.org/10.56294/saludcyt20226

Tharwat, A. (2019). Parameter investigation of support vector machine classifier with kernel functions. Knowledge and Information Systems, 1269-1302. Obtenido de https://link.springer.com/article/10.1007/s10115-019-01335-4 DOI: https://doi.org/10.1007/s10115-019-01335-4

Tiwari, P., Chaudhary, S., Majhi, D., & Mukherjee, B. (2023). Comparing research trends through author-provided keywords with machine extracted terms: A ML algorithm approach using publications data on neurological disorders. Iberoamerican Journal of Science Measurement and Communication, 3(1). https://doi.org/10.47909/ijsmc.36 DOI: https://doi.org/10.47909/ijsmc.36

Tomás Portales Y, Rodríguez Díaz O, Hernández Díaz ER, Álvarez Rosales Y, Dopico Álvarez M. (2023). Manifestaciones emocionales y apoyo social percibido en adultos mayores ante el impacto de la Covid-19. Interdisciplinary Rehabilitation / Rehabilitación Interdisciplinaria, 1.

Valero Medina, J., & Alzate Atehortúa, B. (2019). Comparison of maximum likelihood, support vector machines, and random forest techniques in satellite images classification. . Tectura, 23(59), 13-26. DOI: https://doi.org/10.14483/22487638.14826

Vargas Chanes, D., & Nieto Rivera, P. (2023). The importance of social ties in obtaining employment. AWARI, 3. https://doi.org/10.47909/awari.155 DOI: https://doi.org/10.47909/awari.155

Véliz Capuñay, C. (2020). Aprendizaje Automático: Introducción al Aprendizaje Automático. Fondo Editorial PUCP. Lima-Perú: Fondo Editorial PUCP.

Veliz Moreno Z, Núñez Ravelo E, Arencibia Llanes L, Suarez Castillo IE, Contreras Tamargo Y. (2023). Ansiedad, depresión y estrategias de afrontamiento en pacientes convalescientes de Covid-19. Interdisciplinary Rehabilitation / Rehabilitación Interdisciplinaria, 1.

Zarei, J., Jamshidnezhad, A., Haddadzadeh, S. M., Mohammad, H. A., Cheraghi, M., & Sheikhtaheri, A. (2022). Machine learning models to predict in-hospital mortality among inpatients with COVID-19: Underestimation and overestimation bias analysis in subgroup populations. Journal of Healthcare Engineering,. doi: https://doi.org/10.1155/2022/1644910 DOI: https://doi.org/10.1155/2022/1644910

Zoabi, Y., Deri, R. S., & Shomron, N. (2021). Machine learning-based prediction of COVID-19 diagnosis based on symptoms. Digital Medicine. doi:10.1038/s41746-020-00372-6 DOI: https://doi.org/10.1038/s41746-020-00372-6

Zohair, M., El-Sayed, A., Ashraf, E., Guesh, D., Osama, A. G., Abdallah, A. M., . . . Ibrahim, G. (2021). The COVID-19 pandemic: prediction study based on machine Learning models. ENVIRONMENTAL FACTORS AND THE EPIDEMICS OF COVID-19. doi:10.1007/s11356-021-13824-7 DOI: https://doi.org/10.1007/s11356-021-13824-7

Downloads

Published

20-06-2023

How to Cite

1.
Andrade-Girón D, Carreño-Cisneros E, Mejía-Dominguez C, Velásquez-Gamarra J, Marín-Rodriguez W, Villarreal-Torres H, Meleán-Romero R. Support vector machine with optimized parameters for the classification of patients with COVID-19. EAI Endorsed Trans Perv Health Tech [Internet]. 2023 Jun. 20 [cited 2024 May 26];9:e8. Available from: https://publications.eai.eu/index.php/phat/article/view/3472