Bronchopulmonary Dysplasia Prediction Using Support Vector Machine and LIBSVM

Abstract

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[1] was used as classifier. Results are compared to others gathered in previous work [2] 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[2] 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.

Deep Evolving GMDH-SVM-Neural Network and its Learning for Data Mining Tasks

“…One of the most effective representatives of the shallow neural networks are support vector machines (SVM) [9], [10], [11], [12], [13]. The tuning of support vector machines is provided by using both lazy learning (the activation functions' centers tuning) and optimization procedures (the synaptic weights tuning).…”