TY - JOUR
T1 - Predicting Antimicrobial Resistance Trends Combining Standard Linear Algebra with Machine Learning Algorithms
AU - Castiglione, Filippo
AU - Daugulis, Peteris
AU - Mancini, Emiliano
AU - Oldenkamp, Rik
AU - Schultsz, Constance
AU - Vagale, Vija
N1 - Publisher Copyright: © 2024 University of Latvia. All rights reserved.
PY - 2024
Y1 - 2024
N2 - Antimicrobial resistance prediction is a pivotal ongoing research activity that is currently being explored across various levels. In this context, we present the application of two prediction methods that model the antimicrobial resistance of Neisseria gonorrhoeae on the national level as an outcome of socio-economic processes. The methods use two different implementations of the principal component analysis combined with classification algorithms. Using these two methods, we generated forecasts concerning antimicrobial resistance of Neisseria gonorrhoeae, using publicly available databases encompassing over 200 countries from 1998 to 2021. Both approaches exhibit similar mean absolute averages and correlations when comparing available measurements with predictions. Steps of statistical analysis and applications are discussed, including population-weighted central tendencies, geographical correlations, time trends and error reduction possibilities.
AB - Antimicrobial resistance prediction is a pivotal ongoing research activity that is currently being explored across various levels. In this context, we present the application of two prediction methods that model the antimicrobial resistance of Neisseria gonorrhoeae on the national level as an outcome of socio-economic processes. The methods use two different implementations of the principal component analysis combined with classification algorithms. Using these two methods, we generated forecasts concerning antimicrobial resistance of Neisseria gonorrhoeae, using publicly available databases encompassing over 200 countries from 1998 to 2021. Both approaches exhibit similar mean absolute averages and correlations when comparing available measurements with predictions. Steps of statistical analysis and applications are discussed, including population-weighted central tendencies, geographical correlations, time trends and error reduction possibilities.
KW - AMR prevalence prediction
KW - Neisseria gonorrhoea
KW - PCA
KW - antimicrobial resistance
KW - principal component regression
KW - surveillance
UR - http://www.scopus.com/inward/record.url?scp=85189083335&partnerID=8YFLogxK
U2 - 10.22364/bjmc.2024.12.1.03
DO - 10.22364/bjmc.2024.12.1.03
M3 - Article
SN - 2255-8942
VL - 12
SP - 30
EP - 49
JO - Baltic Journal of Modern Computing
JF - Baltic Journal of Modern Computing
IS - 1
ER -