Studies have been performed to describe the significance of genetic polymorphisms in complications associated with disturbed perinatal adaptation. Due to the large number of interacting factors, the results of classic statistical methods are often inconsistent. The random forest technique (RFT) is a robust nonparametric statistical approach that overcomes this problem through the calculation of the importance of each factor. We used RFT to reanalyze the importance of 24 genetic polymorphisms in the classification of preterm infants (birth weight, 680-1460 g, n = 100) to affected and unaffected groups according to the presence of acute perinatal complications. The accuracy of classification was between 0.5 and 0.8 for each complication when only birth data were considered. However, when genetic polymorphisms with the highest importance scores (ISs) were included in the analysis, the accuracy of classification according overall morbidity, necrotizing enterocolitis (NEC), acute renal failure (ARF), infant respiratory distress syndrome (IRDS), cardiac failure (CF), and patent ductus arteriosus (PDA) improved from 0.69, 0.60, 0.70, 0.72, 0.68, and 0.57 to 0.77, 0.70, 0.76, 0.77, 0.76, and 0.64, respectively. Our findings suggest that genetic polymorphisms identified by RFT as predictors may improve the risk assessment of preterm infants. RFT is a suitable tool to develop risk factor patterns in this population.