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Computer-controlled diabetes disease diagnosis technique based on fuzzy inference structure for insulin-dependent patients

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Abstract

The goal of this study is to the development of an automated closed-loop advice system for intense insulin therapy in clinical practice. We use a Mamdani-type fuzzy logic structure to develop an insulin advisory system. The origin of a long-term high glucose level could be due to several biological variables, with delays in insulin manufacturing, absorption, and activity being among the most common. To incorporate these reasons in the glucose-insulin structure, we adopt a non-linear delay system proposed by the author (Nilam and Rathee Discret Contin Dyn Syst - Ser B 20(9):3115–3129, 2015). The disturbance in glucose concentration due to increment in the values of delay parameters suggested by authors has been regularized using the controller in the present problems. The four separate experiments were undertaken over the structure that are i) no meal consumption, ii) multiple meal intake in a day, iii) atypical meal input, and iv) uncertainties in the model’s parameter. The proposed controller reduces the amplitude of ultradian glucose oscillation as compared to value quoted by other authors (Esna-Ashari et al. J Med Signals Sens 7(1):8–20 2017; Soylu and Danişman Turk J Electr Eng Comput Sci 26(1):172–183, 2018; Wang et al. J Biol Dyn 3(1):22–38, 2009) and yet no control mechanism has been found in the literature for limiting the amplitude of ultradian oscillations of glucose levels. These results support that a fuzzy-based intelligence technique could be used for development of an artificial pancreas of clinical patients.

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Acknowledgements

The authors express their gratitude to the handling editor and anonymous reviewers for their valuable suggestions and comments that improve the manuscript. We are also grateful to the Delhi Technological University for providing financial supports for this research.

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Authors and Affiliations

  1. Department of Applied Mathematics, Delhi Technological University, Delhi, 110042, India

    Ankit Sharma &  Nilam

  2. Cluster Innovation Centre, University of Delhi, Delhi, 110007, India

    Harendra Pal Singh

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  1. Ankit Sharma
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  2. Nilam
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  3. Harendra Pal Singh
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Correspondence to Nilam.

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Sharma, A., Nilam & Singh, H.P. Computer-controlled diabetes disease diagnosis technique based on fuzzy inference structure for insulin-dependent patients. Appl Intell 53, 1945–1958 (2023). https://doi.org/10.1007/s10489-022-03416-4

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