  Diabetes is a major health problem in both developing and developed countries
and its incidence is rising dramatically. In this study, we investigate a novel
automatic approach to diagnose Diabetes disease based on Feature Weighted
Support Vector Machines (FW-SVMs) and Modified Cuckoo Search (MCS). The
proposed model consists of three stages: Firstly, PCA is applied to select an
optimal subset of features out of set of all the features. Secondly, Mutual
Information is employed to construct the FWSVM by weighting different features
based on their degree of importance. Finally, since parameter selection plays a
vital role in classification accuracy of SVMs, MCS is applied to select the
best parameter values. The proposed MI-MCS-FWSVM method obtains 93.58% accuracy
on UCI dataset. The experimental results demonstrate that our method
outperforms the previous methods by not only giving more accurate results but
also significantly speeding up the classification procedure.
