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Sensors 2015, 15(7), 15921-15951; doi:10.3390/s150715921

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

Department of Computer Engineering, Kyung Hee University, Seocheon-dong, Giheung-gu Yongin-si, Gyeonggi-do 446-701, Korea
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Academic Editor: Jesús Fontecha
Received: 7 May 2015 / Revised: 20 June 2015 / Accepted: 25 June 2015 / Published: 3 July 2015
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Abstract

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) restricted one type of diabetes; (2) lack understandability and explanatory power of the techniques and decision; (3) limited either to prediction purpose or management over the structured contents; and (4) lack competence for dimensionality and vagueness of patient’s data. To overcome these issues, this paper proposes a novel hybrid rough set reasoning model (H2RM) that resolves problems of inaccurate prediction and management of type-1 diabetes mellitus (T1DM) and type-2 diabetes mellitus (T2DM). For verification of the proposed model, experimental data from fifty patients, acquired from a local hospital in semi-structured format, is used. First, the data is transformed into structured format and then used for mining prediction rules. Rough set theory (RST) based techniques and algorithms are used to mine the prediction rules. During the online execution phase of the model, these rules are used to predict T1DM and T2DM for new patients. Furthermore, the proposed model assists physicians to manage diabetes using knowledge extracted from online diabetes guidelines. Correlation-based trend analysis techniques are used to manage diabetic observations. Experimental results demonstrate that the proposed model outperforms the existing methods with 95.9% average and balanced accuracies. View Full-Text
Keywords: reasoning; H2RM; rough set theory; rules mining; RBR; prediction; classification; regression; diabetes mellitus; trend analysis reasoning; H2RM; rough set theory; rules mining; RBR; prediction; classification; regression; diabetes mellitus; trend analysis
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Ali, R.; Hussain, J.; Siddiqi, M.H.; Hussain, M.; Lee, S. H2RM: A Hybrid Rough Set Reasoning Model for Prediction and Management of Diabetes Mellitus. Sensors 2015, 15, 15921-15951.

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