Abstract
Due to the threat and increasing trend to diabetes, different approaches to diagnose it have been proposed, so that classification is one of the main techniques. In this article ultimate aim is designing a novel system to diagnose diabetes. To this end, we use an ensemble classifier to apply support vector machine (SVM), k-nearest neighbor (KNN), and whale optimization algorithm (WOA). WOA is responsible for generating weights for each classifier to improve the accuracy of the diabetes classification. For our empirical study, we gathered a dataset of diabetes from medical centers in Iran. The implementation results showed that the designed ensemble classifier achieved the accuracy rate of 83%, which means it improved the accuracy of the best preceding classifier about 5%. Moreover, the designed ensemble classifier based on WOA improved the accuracy about 1% in comparison with PSO that is preceding the WOA in terms of accuracy level.
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Khademi, F., Rabbani, M., Motameni, H. et al. A weighted ensemble classifier based on WOA for classification of diabetes. Neural Comput & Applic 34, 1613–1621 (2022). https://doi.org/10.1007/s00521-021-06481-x
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DOI: https://doi.org/10.1007/s00521-021-06481-x