H2RM: A Hybrid Rough Set Reasoning Model for Prediction and Management of Diabetes Mellitus

@inproceedings{Ali2015H2RMAH,
  title={H2RM: A Hybrid Rough Set Reasoning Model for Prediction and Management of Diabetes Mellitus},
  author={Rahman Ali and Jamil Hussain and Muhammad Hameed Siddiqi and Maqbool Hussain and Sungyoung Lee},
  booktitle={Sensors},
  year={2015}
}
Diabetes is a chronic disease characterized by high blood glucose level that results either from a deficiency of insulin produced by the body, or the body's resistance to the effects of insulin. Accurate and precise reasoning and prediction models greatly help physicians to improve diagnosis, prognosis and treatment procedures of different diseases. Though numerous models have been proposed to solve issues of diagnosis and management of diabetes, they have the following drawbacks: (1… CONTINUE READING

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Key Quantitative Results

  • Experimental results demonstrate that the proposed model outperforms the existing methods with 95.9% average and balanced accuracies. None that the proposed model outperforms the existing methods with 95.9% average and balanced accuracies.
  • Experimental results for the prediction model reveal that performance of the model is 95.91% in classifying diabetes types.

Citations

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  • 2017 IEEE XXIV International Conference on Electronics, Electrical Engineering and Computing (INTERCON)
  • 2017
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Rough-Regression Model for Investigating Product Attributes and Purchase Decision

  • 2018 7th International Conference on Computer and Communication Engineering (ICCCE)
  • 2018

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