A Hybrid GSA-K-Mean Classifier Algorithm to Predict Diabetes Mellitus

Rojalina Priyadarshini, Rabindra Kumar Barik, Nilamadhab Dash, Brojo Kishore Mishra and Rachita Misra
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Rojalina Priyadarshini: School of Computer Science & Engineering, KIIT University, Bhubaneswar, India
Rabindra Kumar Barik: School of Computer Application, KIIT University, Bhubaneswar, India
Nilamadhab Dash: Department of Information Technology. C.V. Raman College of Engineering, Bhubaneswar, India
Brojo Kishore Mishra: Department of Information Technology, C. V. Raman College of Engineering, Bhubaneswar, India
Rachita Misra: Department of Information Technology, C. V. Raman College of Engineering, Bhubaneswar, India

International Journal of Applied Metaheuristic Computing (IJAMC), 2017, vol. 8, issue 4, 99-112

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.

Date: 2017
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