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Applied Sciences
Volume 15
Issue 3
10.3390/app15031132
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Article

Applying Machine Learning Sampling Techniques to Address Data Imbalance in a Chilean COVID-19 Symptoms and Comorbidities Dataset

by
Pablo Ormeño-Arriagada
1,*,
Gastón Márquez
2,
David Araya
3,
Carla Rimassa
4 and
Carla Taramasco
5
1
Escuela de Ingenieria y Negocios, Universidad de Viña del Mar, Viña del Mar 2580022, Chile
2
Departamento de Ciencias de la Computación y Tecnología de la Información, Universidad del Bío-Bío, Chillán 4081112, Chile
3
Instituto de Tecnología para la Innovación en Salud y Bienestar, Facultad de Ingeniería, Universidad Andrés Bello, Viña del Mar 2530959, Chile
4
Facultad de Medicina, Escuela de Fonoaudiología, Camino la Troya S/N, Universidad de Valparaíso, Valparaíso 2360102, Chile
5
Facultad de Ingeniería, Universidad Andrés Bello, Millennium Nucleus on Sociomedicine, Viña del Mar 2520000, Chile
*
Author to whom correspondence should be addressed.
Appl. Sci. 2025, 15(3), 1132; https://doi.org/10.3390/app15031132
Submission received: 29 October 2024 / Revised: 16 November 2024 / Accepted: 21 November 2024 / Published: 23 January 2025
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Abstract

Reliably detecting COVID-19 is critical for diagnosis and disease control. However, imbalanced data in medical datasets pose significant challenges for machine learning models, leading to bias and poor generalization. The dataset obtained from the EPIVIGILA system and the Chilean Epidemiological Surveillance Process contains information on over 6,000,000 patients, but, like many current datasets, it suffers from class imbalance. To address this issue, we applied various machine learning algorithms, both with and without sampling methods, and compared them using different classification and diagnostic metrics such as precision, sensitivity, specificity, likelihood ratio positive, and diagnostic odds ratio. Our results showed that applying sampling methods to this dataset improved the metric values and contributed to models with better generalization. Effectively managing imbalanced data is crucial for reliable COVID-19 diagnosis. This study enhances the understanding of how machine learning techniques can improve diagnostic reliability and contribute to better patient outcomes.
Keywords: machine learning algorithms; COVID-19 diagnosis; imbalanced data; sampling methods; classification metrics; epidemiological dataset; EPIVIGILA system machine learning algorithms; COVID-19 diagnosis; imbalanced data; sampling methods; classification metrics; epidemiological dataset; EPIVIGILA system

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MDPI and ACS Style

Ormeño-Arriagada, P.; Márquez, G.; Araya, D.; Rimassa, C.; Taramasco, C. Applying Machine Learning Sampling Techniques to Address Data Imbalance in a Chilean COVID-19 Symptoms and Comorbidities Dataset. Appl. Sci. 2025, 15, 1132. https://doi.org/10.3390/app15031132

AMA Style

Ormeño-Arriagada P, Márquez G, Araya D, Rimassa C, Taramasco C. Applying Machine Learning Sampling Techniques to Address Data Imbalance in a Chilean COVID-19 Symptoms and Comorbidities Dataset. Applied Sciences. 2025; 15(3):1132. https://doi.org/10.3390/app15031132

Chicago/Turabian Style

Ormeño-Arriagada, Pablo, Gastón Márquez, David Araya, Carla Rimassa, and Carla Taramasco. 2025. "Applying Machine Learning Sampling Techniques to Address Data Imbalance in a Chilean COVID-19 Symptoms and Comorbidities Dataset" Applied Sciences 15, no. 3: 1132. https://doi.org/10.3390/app15031132

APA Style

Ormeño-Arriagada, P., Márquez, G., Araya, D., Rimassa, C., & Taramasco, C. (2025). Applying Machine Learning Sampling Techniques to Address Data Imbalance in a Chilean COVID-19 Symptoms and Comorbidities Dataset. Applied Sciences, 15(3), 1132. https://doi.org/10.3390/app15031132

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