Repository with codes related to various exploratory data analysis (EDA) techniques and application of ML and DL models
This code represents the implementation of our methodology for “Risk factors associated with COVID-19 lethality: A machine learning approach using Mexico database” by Alejandro Carvantes-Barrera, Lorena Díaz-González, Mauricio Rosales-Rivera, and Luis Alberto Chávez-Almazán.
With this code, we generated the images and results of the manuscript [1] for publication in the Journal of Medical Systems.
This repository contains different types of formats. We present the notebook and xlsx files
The python script codes present in this directory has been written by Alejandro Carvantes-Barrera, and Mauricio Rosales-Rivera.
Identifying risk factors associated with COVID-19 lethality is crucial in combating the ongoing pandemic. In this study, we developed lethality predictive models for each epidemiological wave and for the overall dataset using the Extreme Gradient Boosting technique and analyzed them using Shapley values to determine the contribution levels of various features, including demographics, comorbidities, medical units, and recent medical information from confirmed COVID-19 cases in Mexico between February 23, 2020, and April 15, 2022.
In conclusion, this study identified several significant risk factors associated with COVID-19 lethality in Mexico, which could aid policymakers in developing targeted interventions to reduce mortality rates.