Application of support vector machine modeling and graph theory metrics for disease classification

Abstract

Disease classification is a crucial element of biomedical research. Recent studies have demonstrated that machine learning techniques, such as Support Vector Machine (SVM) modeling, produce similar or improved predictive capabilities in comparison to the traditional method of Logistic Regression. In addition, it has been found that social network metrics can provide useful predictive information for disease modeling. In this study, we combine simulated social network metrics with SVM to predict diabetes in a sample of data from the Behavioral Risk Factor Surveillance System (BRFSS). In this dataset, Logistic Regression outperformed SVM with validation data ROC index of 81.8 and 81.7 for models with and without graph metrics, respectively. SVM with a polynomial kernel had validation ROC index of 72.9 and 75.6 for models with and without graph metrics, respectively. Although this did not perform as well as Logistic Regression, the results are consistent with previous studies utilizing SVM to classify diabetes.