Advancing Carbapenem-Resistant K. pneumoniae Risk Factor and Prognosis Analysis: A Comparative Study of Machine Learning Techniques Over Traditional Statistical Method

Abstract

Background The global rise of carbapenem-resistant Klebsiella pneumoniae (CRKP) poses significant treatment challenges, emphasizing the need to understand contributing factors to infections and their impact on patient prognosis. Traditional models like logistic regression often fall short in handling complex, multidimensional datasets integral to antimicrobial resistance (AMR) research, necessitating advanced analytical approaches. Methods This study compares the efficacy of machine learning techniques—specifically, classification trees and neural networks—against traditional statistical models in analysing risk determinants and prognosis factors of AMR. By integrating demographic, medical records, and next-generation sequencing data, we aimed to leverage machine learning's advanced capabilities to manage complex datasets and provide a comprehensive analysis of factors affecting CRKP infections and patient outcomes. Results Our findings indicate that machine learning techniques, particularly decision trees, offer significant advantages over traditional statistical models in analysing clinical risk factors. The integration of machine learning with next-generation sequencing data enhances the understanding of the genetic basis of AMR, thereby facilitating the development of targeted interventions. Conclusions The application of machine learning techniques represents a preferable alternative for analysing AMR risk determinants and prognosis factors. This study underscores the potential of combining advanced analytical methods with genetic data to improve our understanding and management of AMR, highlighting the critical role of machine learning in advancing research in infectious diseases.