Network centrality for the identification of biomarkers in respondent-driven sampling datasets.

Networks science techniques are frequently used to provide meaningful insights into the populations underlying medical and social data. This paper examines SATHCAP, a dataset related to HIV and drug use in three US cities. In particular, we use network measures such as betweenness centrality, closeness centrality, and eigenvector centrality to find central, important nodes in a network derived from SATHCAP data. We evaluate the attributes of these important nodes and create an exceptionality score based on the number of nodes that share a particular attribute. This score, along with the underlying network itself, is used to reveal insight into the attributes of groups that can be effectively targeted to slow the spread of disease. Our research confirms a known connection between homelessness and HIV, as well as drug abuse and HIV, and shows support for the theory that individuals without easy access to transportation are more likely to be central to the spread of HIV in urban, high risk populations.

Improved Classification of Medical Data Using Meta-Best Feature Selection.

Feature selection provides a useful method for reducing the size of large data sets while maintaining integrity, thereby improving the accuracy of neural networks and other classifiers. However, running multiple feature selection models and their accompanying classifiers can make interpreting results difficult. To this end, we present a data-driven methodology called Meta-Best that not only returns a single feature set related to a classification target, but also returns an optimal size and ranks the features by importance within the set. This proposed methodology is tested on six distinct targets from the well-known REGARDS dataset: Deceased, Self-Reported Diabetes, Light Alcohol Abuse Risk, Regular NSAID Use, Current Smoker, and Self-Reported Stroke. This methodology is shown to improve the classification rate of neural networks by 0.056 using the ROC Area Under Curve metric compared to a control test with no feature selection.