Analyses of interactions between single nucleotide polymorphisms (SNPs) have reported significant associations between mitochondrial displacement loops (D-loops) and chronic dialysis diseases. However, the method used to detect potential SNP-SNP interaction still requires improvement. This study proposes an effective algorithm named dynamic center particle swarm optimization k-nearest neighbors (DCPSO-KNN) to detect the SNP-SNP interaction. DCPSO-KNN uses dynamic center particle swarm optimization (DCPSO) to generate SNP combinations with a fitness function designed using the KNN method and statistical verification. A total of 77 SNPs in the mitochondrial D-loop were used to detect the SNP-SNP interactions and the search ability was compared against that of other methods. The detected SNP-SNP interactions were statistically evaluated. Experimental results showed that DCPSO-KNN successfully detects SNP-SNP interactions in two-to-seven-order combinations (positive predictive value (PPV)+negative predictive value (NPV)=1.154 to 1.310; odds ratio (OR)=1.859 to 4.015; 95% confidence interval (95% CI)=1.151 to 4.265; p-value <0.001). DCPSO-KNN can improve the detection ability of SNP-SNP associations between mitochondrial D-loops and chronic dialysis diseases, thus facilitating the development of biomedical applications.