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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Rojalina Priyadarshini & Rabindra Kumar Barik & Nilamadhab Dash & Brojo Kishore Mishra & Rachita Misra, 2017. "A Hybrid GSA-K-Mean Classifier Algorithm to Predict Diabetes Mellitus," International Journal of Applied Metaheuristic Computing (IJAMC), IGI Global, vol. 8(4), pages 99-112, October.
    • Handle: RePEc:igg:jamc00:v:8:y:2017:i:4:p:99-112
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