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    Neural networks-based prediction of insulin resistance by means the homeostatic model assessment without the insulin concentration test

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    FP462.pdf (510.1Kb)
    Date
    2019-07
    Author
    Chacon, Gerardo
    Madriz, Delia
    Bravo, Antonio
    Pardo, Aldo
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    Abstract
    Tissues insulin sensitivity has been estimated using the homeostasis model assessment. The insulin resistance is thus calculated from the plasma insulin and glucose concentrations. However, the insulin testing is an expensive test. Here, a computational approach based on neural networks for predicting the insulin resistance index through the homeostasis model assessment without considering the insulin testing results is proposed. A dataset of the prevalence study of metabolic syndrome (MS) is used to develop our approach. A total of 1919 subjects is used. The dataset if randomly split into a training set, a testing set, and a validating set for prediction approach performance assessment. Two of the neural networks commonly used in medical application are selected as functional predictors. The indexes obtained using the predictors are compared with the homeostasis model assessment-based index reported on the used dataset. From the comparison, one of neural networks-based approaches is considered the best predictor.
    URI
    http://dx.doi.org/10.18687/LACCEI2019.1.1.462
    http://axces.info/handle/10.18687/20190101_462
    URI Others
    http://laccei.org/LACCEI2019-MontegoBay/meta/FP462.html
    Copyright
    https://laccei.org/blog/copyright-laccei-papers/
    Track
    Software Engineering, Telecommunications, Cybersecurity and Computational tools
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    • 2019 LACCEI - Montego Bay, Jamaica

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