Kernel Based Relevance Vector Machine for Classification of Diseases

Motivated by improvements of diseases and cancers depiction that will be facilitated by an ability to predict the related syndrome occurrence; this work employs a data-driven approach to developing cancer classification/prediction models using Relevance Vector Machine (RVM), a probabilistic kernel-based learning machine. Drawing from the work of Bertrand Luvision, Chao Dong, and the outcome result classification of electrocardiogram signals by S. Karpagachelvi ,which show the superiority of the RVM approach as compared to traditional classifiers, the problem addressed in this research is to design a program of piping components together in a graphic workflows which could help improve the accuracy classification/regression of two models structure methods (Support vector machines and kernel based Relevance Vector machines) for better prediction performance of related diseases and then make a comparison among both methods using clinical data. Would the application of relevance vector machine on these data classification improve their coverage. We developed a hierarchical Bayesian model for binary and bivariate data classification using the RBF, sigmoid kernel, with different parameterization and varied threshold. The parameters of the kernel function are considered as model parameters. The finding results allow us to conclude that RVM is almost equal to SVM on training efficiency and classification accuracy, but RVM performs better on sparse property, generalization ability, and decision speed. Meanwhile, the use of RVM raise some issues due to the fact that it used less support vectors but it trains much faster for non-linear kernel than SVM-light. Finally, we test those approaches on a corpus of public release phenotype data. Further research to improve the accuracy prediction with more patients' data is needed. Appendices provide the SVM and RVM derivation in detail. One important area of focus is the development of models for predicting cancers. Keywords: Support Vector Machines, Relevance Vector Machine, Rapidminer, Tanagra, Accuracy's values