Abstract
Diabetes mellitus is a very serious human health problem. Every year the total number of cases is increasing rapidly. The advancement in the machine learning technologies can help in early and accurate detection of the disease. Therefore, an efficient and very fast diabetes prediction model is proposed in this paper using ridge regression extreme learning machine classifier and firefly optimization algorithm for the optimization of the weight vectors. The PIMA Indian Diabetic Database is used for the training and testing of the model. The maximum achieved accuracy, sensitivity and specificity are 93.4%, 97.5% and 85.72%, respectively. The results of the model are compared with two popular methods, support vector machine (SVM) and extreme learning machine (ELM), and it shows that the proposed method outperforms SVM and ELM.
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Das, P., Nanda, S. (2021). An Improved Ridge Regression-Based Extreme Learning Machine for the Prediction of Diabetes. In: Sabut, S.K., Ray, A.K., Pati, B., Acharya, U.R. (eds) Proceedings of International Conference on Communication, Circuits, and Systems. Lecture Notes in Electrical Engineering, vol 728. Springer, Singapore. https://doi.org/10.1007/978-981-33-4866-0_66
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DOI: https://doi.org/10.1007/978-981-33-4866-0_66
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