Diabetes is a disease that is gaining popularity on daily basis in recent times globally and among different age groups. Diabetes causes damage to nerves, blood vessels, kidney, and retina. Machine learning techniques have proved to be very effective in detecting diabetes. In this study, we applied the Non-Nested Generalisation exemplar classifiers on Pima Indians diabetes dataset to effectively and efficiently classify whether patients are having diabetes or not. Our proposed algorithm proved to be highly effective and efficient with a resultant classification accuracy of 100%, very low false positive rate (0.00) and very high true positive rate of 1.00. All experiments are conducted on WEKA data mining and machine learning simulation environment.
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