Key result
An artificial neural network model using 11 clinical symptoms and comorbidities accurately detected COVID-19 positive cases, achieving an F1 score of 0.98 and an area under the curve of 0.99.
Why the study?
It was important to determine which symptoms presented by positive cases of SARS-Cov2 infection are the best predictors using artificial intelligence.
Does a machine learning model using 11 symptoms accurately detect COVID-19 positive cases?
Cross-Sectional (n=5,434)
Does a machine learning model using 11 symptoms accurately detect COVID-19 positive cases?
Effect estimate: F1 score 0.98, auROC 0.99
A simple machine learning model based on 11 clinical symptoms can highly accurately predict COVID-19 positivity.
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.
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Ramirez-Bautista et al. (2023) conducted a cross-sectional in COVID-19 (n=5,434). Artificial Neural Network diagnostic model was evaluated on Detection of COVID-19 positive cases (F1 score 0.98, auROC 0.99). An artificial neural network model using 11 clinical symptoms and comorbidities accurately detected COVID-19 positive cases, achieving an F1 score of 0.98 and an area under the curve of 0.99.
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