Version 1
: Received: 7 October 2021 / Approved: 8 October 2021 / Online: 8 October 2021 (08:30:13 CEST)
How to cite:
Riaño-Moreno, J.; Romero-Leiton, J. P.; Prieto, K. Governance and Socioeconomic Factors Contributing to Antimicrobial Resistance in European Countries: A Data Panel and Machine-Learning Analysis. Preprints2021, 2021100127. https://doi.org/10.20944/preprints202110.0127.v1
Riaño-Moreno, J.; Romero-Leiton, J. P.; Prieto, K. Governance and Socioeconomic Factors Contributing to Antimicrobial Resistance in European Countries: A Data Panel and Machine-Learning Analysis. Preprints 2021, 2021100127. https://doi.org/10.20944/preprints202110.0127.v1
Riaño-Moreno, J.; Romero-Leiton, J. P.; Prieto, K. Governance and Socioeconomic Factors Contributing to Antimicrobial Resistance in European Countries: A Data Panel and Machine-Learning Analysis. Preprints2021, 2021100127. https://doi.org/10.20944/preprints202110.0127.v1
APA Style
Riaño-Moreno, J., Romero-Leiton, J. P., & Prieto, K. (2021). Governance and Socioeconomic Factors Contributing to Antimicrobial Resistance in European Countries: A Data Panel and Machine-Learning Analysis. Preprints. https://doi.org/10.20944/preprints202110.0127.v1
Chicago/Turabian Style
Riaño-Moreno, J., Jhoana P. Romero-Leiton and Kernel Prieto. 2021 "Governance and Socioeconomic Factors Contributing to Antimicrobial Resistance in European Countries: A Data Panel and Machine-Learning Analysis" Preprints. https://doi.org/10.20944/preprints202110.0127.v1
Abstract
The aim of this work is to explain the behaviour of the multiresistance percentage of Pseudomona aeruginosa in some countries of Europe through a multivariate statistical analysis and machine learning validation, using data from the European Antimicrobial Resistance Surveillance System, the World Health Organization and the World Bank. First, we will use a descriptive analysis and a principal components analysis. Then, we use a k-means clustering to determine the countries and regions that are most affected by the antibiotic resistance. Second, we expand the database by adding some socioeconomic, governance and antibiotic-consumption variables. We then run a data panel regression analysis to determine some functions that relates the multiresistance percentage with those new variables. Finally, we use machine learning techniques to validate a pooling panel data case, using XGBoost and random forest algorithms. The results of the data panel analysis indicate that the most important variables for the multiresistance percentage are corruption control and the rule of law. Similar results are found with the machine learning validation analysis, where the human development index is an additional important variable for the multiresistance percentage.
Keywords
Descriptive analysis; principal components analysis; k-means clustering; data panel regression method; machine learning; XGBoost algorithms; random forest algorithms
Subject
Computer Science and Mathematics, Applied Mathematics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.