TY - JOUR
T1 - Detection and risk assessment of COVID-19 through machine learning
AU - Luna-Benoso, B.
AU - Martínez-Perales, J. C.
AU - Cortés-Galicia, J.
AU - Morales-Rodríguez, U. S.
N1 - Publisher Copyright:
© 2024 The Authors. Published by IASE. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
PY - 2024/1
Y1 - 2024/1
N2 - COVID-19, also known as coronavirus disease, is caused by the SARS-CoV-2 virus. People infected with COVID-19 may show a range of symptoms from mild to severe, including fever, cough, difficulty breathing, tiredness, and nasal congestion, among others. The goal of this study is to use machine learning to identify if a person has COVID-19 based on their symptoms and to predict how severe their illness might become. This could lead to outcomes like needing a ventilator or being admitted to an Intensive Care Unit. The methods used in this research include Artificial Neural Networks (specifically, Multi-Layer Perceptrons), Classification and Regression Trees, and Random Forests. Data from the National Epidemiological Surveillance System of Mexico City was analyzed. The findings indicate that the MultiLayer Perceptron model was the most accurate, with an 87.68% success rate. It was best at correctly identifying COVID-19 cases. Random Forests were more effective at predicting severe cases and those requiring Intensive Care Unit admission, while Classification and Regression Trees were more accurate in identifying patients who needed to be put on a ventilator.
AB - COVID-19, also known as coronavirus disease, is caused by the SARS-CoV-2 virus. People infected with COVID-19 may show a range of symptoms from mild to severe, including fever, cough, difficulty breathing, tiredness, and nasal congestion, among others. The goal of this study is to use machine learning to identify if a person has COVID-19 based on their symptoms and to predict how severe their illness might become. This could lead to outcomes like needing a ventilator or being admitted to an Intensive Care Unit. The methods used in this research include Artificial Neural Networks (specifically, Multi-Layer Perceptrons), Classification and Regression Trees, and Random Forests. Data from the National Epidemiological Surveillance System of Mexico City was analyzed. The findings indicate that the MultiLayer Perceptron model was the most accurate, with an 87.68% success rate. It was best at correctly identifying COVID-19 cases. Random Forests were more effective at predicting severe cases and those requiring Intensive Care Unit admission, while Classification and Regression Trees were more accurate in identifying patients who needed to be put on a ventilator.
KW - Artificial neural networks
KW - COVID-19
KW - Decision trees
KW - Machine learning
KW - Random forests
UR - http://www.scopus.com/inward/record.url?scp=85188120327&partnerID=8YFLogxK
U2 - 10.21833/ijaas.2024.01.025
DO - 10.21833/ijaas.2024.01.025
M3 - Artículo
AN - SCOPUS:85188120327
SN - 2313-626X
VL - 11
SP - 207
EP - 216
JO - International Journal of Advanced and Applied Sciences
JF - International Journal of Advanced and Applied Sciences
IS - 1
ER -