Machine learning to predict ceftriaxone resistance using single nucleotide polymorphisms within a global database of Neisseria gonorrhoeae genomes

ABSTRACTAntimicrobial resistance (AMR) in Neisseria gonorrhoeae is an urgent global health issue. Machine learning (ML) is a powerful tool that can aid in identifying mutations and predicting their impact on AMR. The study aimed to use ML models to predict ceftriaxone susceptibility and decreased susceptibility (S/DS). A public database of N. gonorrhoeae genomes with minimum inhibitory concentration (MIC) data was used to evaluate seven ML models using 97 single nucleotide polymorphisms (SNPs) known to be associated with ceftriaxone resistance. Ceftriaxone MICs ≤ 0.064 mg/L were classified as susceptible, and ceftriaxoneMICs > 0.064 mg/L were classified as DS. The contributions of individual SNPs to predict S/DS were calculated using SHapley Additive exPlanation (SHAP) values. An ML model was retrained using different combinations of SNPs with the highest SHAP values. The performance of ML models was assessed using different metrics including area under the curve (AUC) and balanced accuracy (bAcc). The ML analyses included 9,540 N. gonorrhoeae genomes; 368 (0.04%) were classified as DS. Of the models evaluated, the model trained with a random forest classifier had the highest performance (AUC 0.965; bAcc 0.926). A model retrained the top five SNPs, according to SHAP values, demonstrated a similar performance (AUC 0.916; bAcc 0.879) as the model with 97 SNPs. An ML approach using mutations in N. gonorrhoeae can be used to predict S/DS to ceftriaxone. The results highlight a practical application of ML to identify mutations most associated with S/DS to ceftriaxone, which can aid in the development of assays to predict AMR.IMPORTANCEAntimicrobial resistance in Neisseria gonorrhoeae is an urgent global health issue. The objectives of the study were to use a global collection of 12,936 N. gonorrhoeae genomes from the PathogenWatch database to evaluate different machine learning models to predict ceftriaxone susceptibility/decreased susceptibility using 97 mutations known to be associated with ceftriaxone resistance. We found the random forest classifier model had the highest performance. The analysis also reported the relative contributions of different mutations within the ML model predictions, allowing for the identification of the mutations with the highest importance for ceftriaxone resistance. A machine learning model retrained with the top five mutations performed similarly to the model using all 97 mutations. These results could aid in the development of molecular tests to detect resistance to ceftriaxone in N. gonorrhoeae. Moreover, this approach could be applied to building and evaluating machine learning models for predicting antimicrobial resistance in other pathogens.