Medical classification, diagnosis and prediction of Type 2 diabetes mellitus disorder and its treatment are nowadays growing holistically with the innovations, technology and solutions are obtained with high level of accuracy and performance. Much of research is now being poured for this problem domain and optimal solutions are now being attained with sustainable development in the area. Yet more and more are steadily shed by many researchers in this area for refining research outcomes further. However existing approaches may exhibit good solutions for addressing diabetes mellitus problem, some in terms of improve performance and throughput metrics over others to obtain critical results for the problem of selection. Classification is the backbone behind every work tried thus far in the above said domain. There is scope for further attainment of success in the above direction, if optimization techniques are admixed with existing classifying approaches for better performance. Here it is proposed a mechanism for classification and prediction of treatment for Type 2 diabetes mellitus through supervised learning of classification as being Support Vector Machine and to obtain improved performance through the augment, applying Artificial Bee Colony optimization algorithm to it. The experimental results show that the proposed work is productive9 more in terms of performance and accuracy rather than the existing archetypes.
Volume 11 | 07-Special Issue
Pages: 1175-1180