Computational Intelligence for HIV Modelling

In this paper, we compare computational intelligence methods to analyze HIV in order to investigate which network is best suited for HIV classification. The methods analyzed are autoencoder multi-layer perceptron (MLP), autoencoder radial basis functions (RBF), support vector machines (SVM) and neuro-fuzzy models (NFM). The autoencoder multi-layer perceptron yields the highest accuracy of 92% amongst all the models studied. The autoencoder radial basis function model has the shortest computational time but yields one of the lowest accuracies of 82%. The SVM model yields the worst accuracy of 80%, as well as the worst computational time of 203s. The NFM yields an accuracy of 86%, which is the second highest accuracy. The NFM, however, offers rules, which gives interpretation of the data. The area under the receiver operating characteristics curve for the MLP model is 0.86 compared to an area under the curve of 0.87 for the RBF model, and 0.82 for the neuro- fuzzy model. The autoencoder MLP network model for HIV classification, is thus found to outperform the other network models and is a much better classifier.