Análisis de Patrones e Incidencias de Hipertensión Arterial en Estudiantes Universitarios de Manta en el Contexto Post-COVID-19.

Abstract

This thesis aims to analyze the patterns and incidences of arterial hypertension in university students from Manta, Ecuador, in the post-COVID-19 context. To achieve this, the CRISP-DM methodology is employed, which covers everything from understanding the problem to the evaluation and deployment of the predictive model. Through this methodology, detailed data was collected on the factors influencing students' cardiovascular health, including aspects such as physical activity, eating habits, alcohol consumption, smoking, and other relevant factors. Subsequently, an exploratory data analysis was conducted using visualization techniques such as confusion matrix, PCA, box plot, among others. For modeling, machine learning algorithms such as Random Forest, Gradient Boosting, Logistic Regression, and CN2 Rule Induction were implemented with the goal of identifying relevant patterns in the data. Additionally, an interactive dashboard was designed to clearly visualize hypertension statistics, providing accessible information for both health experts and the general public. The results obtained will help generate recommendations for interventions and public health policies aimed at preventing and controlling hypertension in the university population. This thesis contributes to the knowledge of the post-pandemic impact on cardiovascular health.