Ndaba, NPillay, AWEzugwu, AE2019-03-292019-03-292018-07Ndaba, M., Pillay, A.W., Ezugwu, A.E. 2018. An improved generalized regression neural network for Type II Diabetes classification. Proceedings of the 18th International Conference on Computational Science and Its Applications (ICCSA 2018), 2-5 July 2018, Melbourne, VIC, Australia, pp 659-671.https://link.springer.com/chapter/10.1007%2F978-3-319-95171-3_52http://hdl.handle.net/10204/10878Copyright: 2018 Springer. Due to copyright restrictions, the attached PDF file only contains the abstract of the full text item. For access to the full text item, kindly consult the publisher's website.This paper proposes an improved Generalized Regression Neural Network (KGRNN) for the diagnosis of type II diabetes. Diabetes, a widespread chronic disease, is a metabolic disorder that develops when the body does not make enough insulin or is unable to use insulin effectively. Type II diabetes is the most common type and accounts for an estimated 90% of cases. The novel KGRNN technique reported in this study uses an enhanced K-Means clustering technique (CVE-K-Means) to produce cluster centers (centroids) that are used to train the network. The technique was applied to the Pima Indian diabetes dataset, a widely used benchmark dataset for Diabetes diagnosis. The technique outperforms the best known GRNN techniques for Type II diabetes diagnosis in terms of classification accuracy and computational time and obtained a classification accuracy of 86% with 83% sensitivity and 87% specificity. The Area Under the Receiver Operating Characteristic Curve (ROC) of 87% was obtained.enDiabetes classificationArtificial neural networksGeneralized neural networksBook ChapterNdaba, N., Pillay, A., & Ezugwu, A. (2018). An improved generalized regression neural network for Type II Diabetes classification., <i>Worklist;22101</i> Springer. http://hdl.handle.net/10204/10878Ndaba, N, AW Pillay, and AE Ezugwu. "An improved generalized regression neural network for Type II Diabetes classification" In <i>WORKLIST;22101</i>, n.p.: Springer. 2018. http://hdl.handle.net/10204/10878.Ndaba N, Pillay A, Ezugwu A. An improved generalized regression neural network for Type II Diabetes classification.. Worklist;22101. [place unknown]: Springer; 2018. [cited yyyy month dd]. http://hdl.handle.net/10204/10878.TY - Book Chapter AU - Ndaba, N AU - Pillay, AW AU - Ezugwu, AE AB - This paper proposes an improved Generalized Regression Neural Network (KGRNN) for the diagnosis of type II diabetes. Diabetes, a widespread chronic disease, is a metabolic disorder that develops when the body does not make enough insulin or is unable to use insulin effectively. Type II diabetes is the most common type and accounts for an estimated 90% of cases. The novel KGRNN technique reported in this study uses an enhanced K-Means clustering technique (CVE-K-Means) to produce cluster centers (centroids) that are used to train the network. The technique was applied to the Pima Indian diabetes dataset, a widely used benchmark dataset for Diabetes diagnosis. The technique outperforms the best known GRNN techniques for Type II diabetes diagnosis in terms of classification accuracy and computational time and obtained a classification accuracy of 86% with 83% sensitivity and 87% specificity. The Area Under the Receiver Operating Characteristic Curve (ROC) of 87% was obtained. DA - 2018-07 DB - ResearchSpace DP - CSIR KW - Diabetes classification KW - Artificial neural networks KW - Generalized neural networks LK - https://researchspace.csir.co.za PY - 2018 T1 - An improved generalized regression neural network for Type II Diabetes classification TI - An improved generalized regression neural network for Type II Diabetes classification UR - http://hdl.handle.net/10204/10878 ER -