Bronchopulmonary Dysplasia Prediction Using Support Vector Machine and Logit Regression

The paper presents BPD (Bronchopulmonary Dysplasia) prediction for extremely premature infants after their first week of life. SVM (Support Vector Machine) and LR (Logit Regression) are used as classifiers. Data was collected thanks to the Neonatal Intensive Care Unit of The Department of Pediatrics at Jagiellonian University Medical College and includes 109 patients with birth weight less than or equal to 1500g. Fourteen different risk factor parameters were considered and all 214 combinations were analyzed. Classifier based on six feature LR model provides accuracy up to 82%, while SVM one turns out to be generally much worse, providing in best case scenario 80% of accuracy. In addition, the article discusses the influence of the model parameters selection on prediction quality.