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

Author:

Priyadarshini Rojalina1,Barik Rabindra Kumar2,Dash Nilamadhab3,Mishra Brojo Kishore4ORCID,Misra Rachita4

Affiliation:

1. School of Computer Science & Engineering, KIIT University, Bhubaneswar, India

2. School of Computer Application, KIIT University, Bhubaneswar, India

3. Department of Information Technology. C.V. Raman College of Engineering, Bhubaneswar, India

4. 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.

Publisher

IGI Global

Subject

Decision Sciences (miscellaneous),Computational Mathematics,Computational Theory and Mathematics,Control and Optimization,Computer Science Applications,Modeling and Simulation,Statistics and Probability

Reference43 articles.

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