Application of Support Vector Machine Modeling and Graph Theory Metrics for Disease Classification

TL;DR

This study explores combining social network metrics with Support Vector Machine modeling to predict diabetes, comparing its performance to Logistic Regression using real-world data, and finds Logistic Regression generally performs better.

Contribution

It introduces the integration of social network metrics with SVM for disease classification, specifically diabetes, and compares its effectiveness to traditional Logistic Regression.

Findings

Logistic Regression outperformed SVM in ROC index.

SVM with polynomial kernel showed moderate performance.

Graph metrics provided some predictive value but did not surpass Logistic Regression.

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. In this dataset, Logistic Regression outperformed SVM with ROC index of 81.8 and 81.7 for models with and without graph metrics, respectively. SVM with a polynomial kernel had 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.