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Diabetes is a major health problem in both developing and developed countriesand its incidence is rising dramatically. In this study, we investigate a novelautomatic approach to diagnose Diabetes disease based on Feature WeightedSupport Vector Machines (FW-SVMs) and Modified Cuckoo Search (MCS). Theproposed model consists of three stages: Firstly, PCA is applied to select anoptimal subset of features out of set of all the features. Secondly, MutualInformation is employed to construct the FWSVM by weighting different featuresbased on their degree of importance. Finally, since parameter selection plays avital role in classification accuracy of SVMs, MCS is applied to select thebest parameter values. The proposed MI-MCS-FWSVM method obtains 93.58% accuracyon UCI dataset. The experimental results demonstrate that our methodoutperforms the previous methods by not only giving more accurate results butalso significantly speeding up the classification procedure.

Automatic Detection of Diabetes Diagnosis using Feature Weighted Support Vector Machines based on Mutual Information and Modified Cuckoo Search