Marshall, JonathanSmith, Helen2025-07-162025-07-162025-07-14https://mro.massey.ac.nz/handle/10179/73186Foodborne diseases, such as campylobacteriosis, represent a significant risk to public health. Preventing the spread of Campylobacter species requires knowledge of sources of human infection. Current methods of source attribution are designed to be used with a small number of genes, such as the seven housekeeping genes of the original multilocus sequence typing (MLST) scheme, and encounter issues when presented with whole genome data. Higher resolution data, however, offers the potential to differentiate within source groups (i.e., between different ruminant species in addition to differentiating between ruminants and poultry), which is poorly achieved with current methods. Random forest is a tree-based machine learning algorithm which is suitable for analysing data sets with large numbers of predictor variables, such as whole genome sequencing data. A known issue with tree-based predictive models occurs when new levels of a variable are present in an observation for prediction which were not present in the set of observations with which the model was trained. This is almost certain to occur with genomic data, which has a potentially ever-growing set of alleles for any single gene. This thesis investigates the use of ordinal encoding categorical variables to address the ‘absent levels’ problem in random forest models. Firstly, a method of encoding is adapted, based on correspondence analysis (CA) of a class by level contingency table, to be unbiased in the presence of absent levels. Secondly, a new method of encoding is introduced which utilises a set of supplementary information on the category levels themselves (i.e., the sequence information of alleles) and encodes them, as well as any new levels, according to their similarity or dissimilarity to each other via the method of principal coordinates analysis (PCO). Thirdly, based on the method of canonical analysis of principal coordinates (CAP), the encoding information of the levels from the CA on the contingency table is combined with the encoding information of the levels from the PCO on the dissimilarity matrix of the supplementary levels information, with a classical correspondence analysis (CCorA). Potential issues when using out-of-bag (OOB) data following variable encoding are then explored and an adaptation to the holdout variable importance method is introduced which is suitable for use with all methods of encoding. This thesis finishes by applying the CAP method of encoding to a random forest predictive model for source attribution of whole genome sequencing data from the Source Assigned Campylobacteriosis in New Zealand (SACNZ) study. The advantage of adding core genes and accessory genes as predictor variables is investigated, and the attribution results are compared to the results from a previously published study which used the asymmetric island model on the same set of isolates and the seven MLST genes.© The Authorrandom forest, absent levels, categorical predictors, variable encoding, source attribution, campylobacterMachine learningStatistical methodsData miningComputer programsPredictive analyticsCampylobacter infections490503 Computational statistics