This research aimed to formulate a Bayesian model based on the Naive Bayes algorithm, to predict morbidity in neonates in a case study of pregnant mothers in Metropolitan Lima. The study uses mathematical algorithms for the exploitation of information in prevention of possible health-related problems. 13 predictive nutritional variables proposed by Krauss were raised. The model consists first of all, in the collection of the nutritional information in a controlled way of the pregnant women involved, then, the information is analyzed to determine the relationship of the most influential variables for the model, then the Bayesian model of acyclic characteristic was constructed and directed composed of nodes and edges, because the variables directly affected to the morbidity of the neonate are known and finally the model affected by the statistical results of the nutritional variables is va...

This research aimed to formulate a Bayesian model based on the Naive Bayes algorithm, to predict morbidity in neonates in a case study of pregnant mothers in Metropolitan Lima. The study uses mathematical algorithms for the exploitation of information in prevention of possible health-related problems. 13 predictive nutritional variables proposed by Krauss were raised. The model consists first of all, in the collection of the nutritional information in a controlled way of the pregnant women involved, then, the information is analyzed to determine the relationship of the most influential variables for the model, then the Bayesian model of acyclic characteristic was constructed and directed composed of nodes and edges, because the variables directly affected to the morbidity of the neonate are known and finally the model affected by the statistical results of the nutritional variables is va...

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. ...