In this study, ant colony optimisation (ACO) algorithm is used to derive near-optimal interactions between a number of single nucleotide polymorphisms (SNPs). This approach is used to discover small numbers of SNPs that are combined into a decision tree or contingency table model. The ACO algorithm is shown to be very robust as it is proven to be able to find results that are discriminatory from a statistical perspective with logical interactions, decision tree and contingency table models for various numbers of SNPs considered in the interaction. A large number of the SNPs discovered here have been already identified in large genome-wide association studies to be related to type II diabetes in the literature, lending additional confidence to the results.