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- W4230782788 abstract "Lots of research has been carried out globally to design a machine classifier which could predict it from some physical and bio-medical parameters. In this work a hybrid machine learning classifier has been proposed to design an artificial predictor to correctly classify diabetic and non-diabetic people. The classifier is an amalgamation of the widely used K-means algorithm and Gravitational search algorithm (GSA). GSA has been used as an optimization tool which will compute the best centroids from the two classes of training data; the positive class (who are diabetic) and negative class (who are non-diabetic). In K-means algorithm instead of using random samples as initial cluster head, the optimized centroids from GSA are used as the cluster centers. The inherent problem associated with k-means algorithm is the initial placement of cluster centers, which may cause convergence delay thereby degrading the overall performance. This problem is tried to overcome by using a combined GSA and K-means." @default.
- W4230782788 created "2022-05-11" @default.
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- W4230782788 date "2020-01-01" @default.
- W4230782788 modified "2023-09-27" @default.
- W4230782788 title "A Hybrid GSA-K-Mean Classifier Algorithm to Predict Diabetes Mellitus" @default.
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- W4230782788 doi "https://doi.org/10.4018/978-1-7998-2460-2.ch030" @default.
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