Mortality Analysis of Patients with COVID-19 in Mexico Based on Risk Factors Applying Machine Learning Techniques

Abstract

The new pandemic caused by the COVID-19 virus has generated an overload in the quality of medical care in clinical centers around the world. Causes that originate this fact include lack of medical personnel, infrastructure, medicines, among others. The rapid and exponential increase in the number of patients infected by COVID-19 has required an efficient and speedy prediction of possible infections and their consequences with the purpose of reducing the health care quality overload. Therefore, intelligent models are developed and employed to support medical personnel, allowing them to give a more effective diagnosis about the health status of patients infected by COVID-19. This paper aims to propose an alternative algorithmic analysis for predicting the health status of patients infected with COVID-19 in Mexico. Different prediction models such as KNN, logistic regression, random forests, ANN and majority vote were evaluated and compared. The models use risk factors as variables to predict the mortality of patients from COVID-19. The most successful scheme is the proposed ANN-based model, which obtained an accuracy of 90% and an F1 score of 89.64%. Data analysis reveals that pneumonia, advanced age and intubation requirement are the risk factors with the greatest influence on death caused by virus in Mexico.

Complicidad del fenotipo clínico preexistente con el resultado de muerte en los pacientes de COVID-19

“…12 These comorbidities present a low grade chronic inflammation producing adipokines and cytokines promoting an adequate scenario for susceptibility to infection such as SARS-CoV-2, and therefore this virus could enhance the pre-existing inflammation resulting in a more severe COVID-19 outcome. 13 The mortality risk has been reported as increased in patients COVID-19 with intubation requirement, 14 in Mexico, having an age ≥ 60 years and hypertension had been reported like factors risk to death, however, in our study, we found that overweight and T2D were the main mortality risk factors risk, this reflect the public health problem of high prevalence of non-communicable diseases we suffer in Mexico, and the imperious necessity for their control.…”

Factors Associated with the Clinical Severity and Disease Burden of COVID-19 Caused by Omicron BA.2 in Shanghai and Hong Kong, China

“…Several studies have demonstrated that the clinical severity and disease burden of COVID-19 are associated with age structure, vaccination status, underlying disease status, accessibility of medical resources, and other factors [13][14][15]. Possible explanatory variables and their definitions are shown in S7 Table . We first explored possible factors by calculating Spearman correlation coefficients.…”

“…Subsequently, we conducted random forest regression to explore the inf luence of multiple factors simultaneously [13]. Furthermore, we used the eXtreme Gradient Boosting (XGBoost) [16] model to explore the overall correlations for whole samples and the specific correlation between factors and indicators in Shanghai and Hong Kong, China.…”

Machine Learning Algorithms Application in COVID-19 Disease: A Systematic Literature Review and Future Directions

“…Finally, Figure 2 shows the percentages of use of the metrics in the articles reviewed in this work. Regarding the performance of the algorithms used in the prediction models, many studies evaluate and compare two or more metrics (we found in the study papers that they evaluated at least five algorithms [59,60,67,76,77,86]), and to facilitate the understanding of the results, this analysis selected the metrics that reported the best performance within each study. Consequently, Table 6 contains the average of the best values presented by the metric in each study.…”