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Neural Computing and Applications

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06.04.2017 | Original Article

Analysis of computational intelligence techniques for diabetes mellitus prediction

verfasst von: Ashok Kumar Dwivedi

Erschienen in: Neural Computing and Applications | Ausgabe 12/2018

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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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Titel: Analysis of computational intelligence techniques for diabetes mellitus prediction
verfasst von: Ashok Kumar Dwivedi
Publikationsdatum: 06.04.2017
Verlag: Springer London
Erschienen in: Neural Computing and Applications / Ausgabe 12/2018
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-017-2969-9

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