Abstract

RAMIREZ-BAUTISTA, Julian Andres et al. Classification of COVID-19 associated symptomatology using machine learning. Dyna rev.fac.nac.minas [online]. 2023, vol.90, n.226, pp.36-43.  Epub Feb 12, 2024. ISSN 0012-7353.  https://doi.org/10.15446/dyna.v90n226.105616.

The health situation caused by the SARS-Cov2 coronavirus, posed major challenges for the scientific community. Advances in artificial intelligence are a very useful resource, but it is important to determine which symptoms presented by positive cases of infection are the best predictors. A machine learning approach was used with data from 5,434 people, with eleven symptoms: breathing problems, dry cough, sore throat, running nose, history of asthma, chronic lung, headache, heart disease, hypertension, diabetes, and fever. Based on public data from Kaggle with WHO standardized symptoms. A model was developed to detect COVID-19 positive cases using a simple machine learning model. The results of 4 loss functions and by SHAP values, were compared. The best loss function was Binary Cross Entropy, with a single hidden layer configuration with 10 neurons, achieving an F1 score of 0.98 and the model was rated with an area under the curve of 0.99 aucROC.

Keywords : computer-aided diagnosis: COVID-19; disease diagnosis; machine learning; artificial neural networks.

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