Analysis of computational intelligence techniques for diabetes mellitus prediction

Abstract

Diabetes as a chronic disease is becoming a foremost community health concern worldwide. In developing countries, the diabetic patients are increasing rapidly due to lack of sentience and bad eating habits. So, there is a need of a framework that can effectively diagnose thousands of patients using clinical specifics. This work uses six computational intelligence techniques for diabetes mellitus prediction namely classification tree, support vector machine, logistic regression, naïve Bayes, and artificial neural network. The performance of these techniques was evaluated on eight different classification performance measurements. Moreover, these techniques were appraised on a receiver operative characteristic curve. Classification accuracy of 77 and 78% was achieved by artificial neural network and logistic regression, respectively, with F 1 measure of 0.83 and 0.84.

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Acknowledgements

The author is highly grateful to the Department of Biotechnology, New Delhi for providing support for this work under Bioinformatics Infrastructure Facility of Department of Biotechnology, Ministry of Science and Technology, India at Maulana Azad National Institute of Technology, Bhopal.

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Correspondence to Ashok Kumar Dwivedi.

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Dwivedi, A.K. Analysis of computational intelligence techniques for diabetes mellitus prediction. Neural Comput & Applic 30, 3837–3845 (2018). https://doi.org/10.1007/s00521-017-2969-9

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Keywords

  • Classification tree
  • Artificial neural network
  • Naïve Bayes
  • Logistic regression
  • Diabetes mellitus
  • Support vector machine
  • Classification
  • Machine learning algorithm
  • Treatments