ABSTRACT
SARS-CoV-2 has bought many challenges to the world, socially, economically, and healthy habits. Even to those that have not experienced the sickness itself, and even though it has changed the lifestyle of the people across the world nation wise the effects of COVID-19 need to be analyzed and understood, analyzing a large amount of data is a process by itself, in this document details the analysis of the data collected from México by the Secretary of Health, the data was analyzed by implementing statistics, and classification methods known as K-Means, C&R Tree and TwoStep Cluster, using processed and unprocessed data. With the main emphasis on K-means. The study has the purpose of detecting what makes the highest impact on a person, to get sick, and succumb to the effects of the disease. In the study, it was found that in México the age of risk is at its highest at the age of 57, and the ones at the highest risk of mortality are those with hypertension and obesity, with those that present both at the age of 57 having a 19.37% of death. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
In this project, a proposal is made to contribute to the reduction of COVID-19 infections, investigating how to carry out an electronic and/or digital collection system for public transport in Tijuana in Mexico, in addition to having a more versatile intelligent system for users. This proposal also seeks to reduce soil contamination, using fewer physical inputs. Due to the development of the pandemic that has occurred in recent years, there have been thousands of people infected by the COVID-19 virus (SARS-CoV-2). Due to its rapid spread, it has caused many deaths. Alternatives have been sought to seek the reduction of infections. The objective is to reduce the physical entrances to avoid contagions by manipulation to the touch;the transport routes have been contemplated and the different routes in the locality will be analyzed through artificial intelligence and implement it in all public transport routes. Also a system can be implemented for digital ticket. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
This project's main objective is to discover which are those comorbidities that could lead to a fatal outcome in a patient diagnosed with COVID-19 in the state of Baja California through a classification algorithm using neural networks. For this, a database obtained on the federal government portal by the General Directorate of Epidemiology with a cutoff date of June 8, 2020 was used. Only the records of the residents of Baja California were kept and only the following data: Sex, Municipality, Date of death, Age, all those variables referring to morbidities, Result (Confirmed cases of COVID-19), ICU (If they needed to enter the intensive care unit);also, from the variable of the date also, from the date variable of death, another variable called "Deceased" was generated to categorize whether the patient died or not. The resulting database was imported into the software where the model of the neural network, data preparation was performed and built the neural network model (multilayer perceptron). The dependent variable "Deceased" was selected, as variables the variables referring to the patient's comorbidities and as a covariate the variable of the scalar type Age. For this model, a random partition of the data was carried out, where 70% of the data was assigned for training and the remaining 30% for tests, obtaining a success rate of 82% and an 18 % error.