COVID-19 has caused an economic crisis in the business world, leaving limitations in the continuity of the payment chain, with companies resorting to credit access. This study aimed to determine the optimal machine learning predictive model for the credit risk of companies under the Reactiva Peru Program because of COVID-19. A multivariate regression analysis was applied with four regressor variables (economic sector, granting entity, amount covered, and department) and one predictor (risk level), with a population of 501,298 companies benefiting from the program, under the CRISP-DM methodology oriented especially for data mining projects, with artificial intelligence techniques under the machine learning Lasso and Ridge regression models, with econometric algebraic mathematical verification to compare and validate the predictive models using SPSS, Jamovi, R Studio, and MATLAB software. ...

Objectives: Determine the characterization of the diet in biochemical and anthropometric profiles with the analysis of the principal components in obese Ecuadorian patients. Methods: Descriptive, comparative, longitudinal studies; we had access to the institutional health clinical history database and a study group was formed, they were offered a low-carbohydrate diet. The sample consisted of 110 obese patients from the Hospital of Guayaquil-Ecuador. Results: The patients were between the ages of 25 to 65 years. The results showed a significant loss of BMI (kg/m2) (Δ-2,6±1.9) (p<0,001), waist circumference (cm) (Δ-5,1±4,7) (p<0,001), body fat (%) (Δ-3,6±3,6) (p<0,001), triglycerides (mg/dL) (Δ-25,4±72,9) (p<0,001) and glucose (mg/dL) (Δ-6,8±9,6) (p<0,001). Conclusion: The low carbohydrate diet reduces BMI, waist circumference, body fat, triglycerides and glucose ...

This research has been designed with the objective of analyzing the indicators of teacher motivation and performance to determine a factorial model of prediction in teachers. The methodology corresponds to a quantitative approach of a predictive nature, for this, two instruments were used and they were applied to two populations: the motivation instrument was applied to teachers and the teacher performance instrument, to students of the 4th and 5th year of secondary level. The participants were made up of 59 teachers and 197 students. The KMO index was 0.636, this allowed a factor analysis that follows a methodological process of seven well-defined phases. The results yielded three clusters: extrinsic and transcendental motivation (cluster 1); intrinsic motivation (cluster 2); and teaching performance (cluster 3) in which two eigenvalues ​​greater than 1 were obtained, allowing us to...

This research has been designed with the objective of analyzing the indicators of teacher motivation and performance to determine a factorial model of prediction in teachers. The methodology corresponds to a quantitative approach of a predictive nature, for this, two instruments were used and they were applied to two populations: the motivation instrument was applied to teachers and the teacher performance instrument, to students of the 4th and 5th year of secondary level. The participants were made up of 59 teachers and 197 students. The KMO index was 0.636, this allowed a factor analysis that follows a methodological process of seven well-defined phases. The results yielded three clusters: extrinsic and transcendental motivation (cluster 1); intrinsic motivation (cluster 2); and teaching performance (cluster 3) in which two eigenvalues ​​greater than 1 were obtained, allowing us to...