Resumen

FERNANDEZ CEDENO, Gilberto Horacio; CRUZ FELIPE, Marely del Rosario; ORDONEZ-AVILA, Ermenson Ricardo  y  MOREIRA VELEZ, José Gabriel. Prediction of post covid19 evolution in patients, using big data tools. ReHuSo [online]. 2023, vol.8, n.2, pp.125-136.  Epub 05-Dic-2023. ISSN 2550-6587.  https://doi.org/10.33936/rehuso.v8i2.5911.

This research aims to predict the post COVID-19 evolution in patients at the Portoviejo General Hospital (IESS), identifying similar patterns in the spread of future cases of this disease. As a methodology, a descriptive, retrospective study was carried out, with a quantitative approach and use of the documentary analysis method, where information was extracted from the database of the aforementioned hospital, in the period 2020-2022. For the data analysis, the Orange Data Mining software was used, which is an open-source tool with a wide range of data analysis and machine learning methods. Of the total of 18,316 patients, an intentional sample of 3,678 was used, since they had the data required for analysis. Among the main results, it stands out that the people most likely to have Covid are in the age range between 63 and 70 years; the most exposed sex is the male; The most common symptoms for those affected are respiratory failure and chronic kidney disease, issues that help predict which patients may be more likely to contract the disease. In conclusion, it is highlighted that the application of data mining tools facilitates the prediction and future evolution of diseases such as the one analyzed, facilitating decision-making on the prevention and control of the pandemic for health authorities.

Palabras clave : COVID-19; prediction; data mining; orange data mining; prevention.

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