  Diabetes Mellitus is a major health problem all over the world. Many
classification algorithms have been applied for its diagnoses and treatment. In
this paper, a hybrid algorithm of Modified-Particle Swarm Optimization and
Least Squares- Support Vector Machine is proposed for the classification of
type II DM patients. LS-SVM algorithm is used for classification by finding
optimal hyper-plane which separates various classes. Since LS-SVM is so
sensitive to the changes of its parameter values, Modified-PSO algorithm is
used as an optimization technique for LS-SVM parameters. This will Guarantee
the robustness of the hybrid algorithm by searching for the optimal values for
LS-SVM parameters. The pro-posed Algorithm is implemented and evaluated using
Pima Indians Diabetes Data set from UCI repository of machine learning
databases. It is also compared with different classifier algorithms which were
applied on the same database. The experimental results showed the superiority
of the proposed algorithm which could achieve an average classification
accuracy of 97.833%.
