Multi-class support vector machines for modeling HIV/AIDS treatment adherence using patient data

As the only effective treatment strategy against HIV/AIDS, highly active antiretroviral therapy (HAART) is an extremely promising development in the treatment of HIV/AIDS. The success of HAART highly depends on patient's adherence to this complex treatment. Recent studies found that poor adherence was a major cause of treatment failure and emerging drug resistance, and thus it is important to understand the factors that contribute to good and poor adherence. However, the discovery of factors salient to adherence is constrained by the well known fact that medical data gathering is expensive and thus usually only a limited amount of information is available. Our study of HIV/AIDS treatment adherence is no exception - we only have data on 33 patients. For this reason, we apply the support vector machine (SVM) to model the relationship of nine patient factors to the level of medication adherence. To establish the baseline performance, we first randomly generated test data sets of comparable sample size and data dimension to the patient pool. A SVM was evaluated using the test data. The SVM was then applied to the real patient data. Finally, a three-layer neural network with back propagation learning was applied to the patient data as well as the test data. The results show that the SVM performed reasonably well and significantly outperformed the neural networks. Our work demonstrates that SVM techniques can be effective in quantitatively modeling complex relationships important in clinical medicine even when the data set size is very small by industrial standards. To our knowledge, this is the first application of a SVM to HIV/AIDS.