Bronchopulmonary Dysplasia Prediction Using Support Vector Machine and LIBSVM
Marcin Ochab, Wiesław Wajs
Citation: Proceedings of the 2014 Federated Conference on Computer Science and Information Systems, M. Ganzha, L. Maciaszek, M. Paprzycki (eds). ACSIS, Vol. 2, pages 201–208 (2014)
Abstract. The paper presents BPD (Bronchopulmonary Dysplasia) prediction for extremely premature infants after their first week of life. SVM (Support Vector Machine) algorithm implemented in LIBSVM was used as classifier. Results are compared to others gathered in previous work where LR (Logit Regression) and Matlab environment SVM implementation were used. Fourteen different risk factor parameters were considered and due to the high computational complexity only 3375 random combinations were analysed. Classifier based on eight feature model provides the highest accuracy which was 82.60\%. The most promising 5-feature model which gathered 82.23\% was reasonably immune to random data changes and consistent with LR results. The main conclusion is that unlike Matlab SVM implementation, LIBSVM can be successfully used in considered problem but it is less stable than LR. In addition, the article discusses influence of the model parameters selection on prediction quality.