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

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, progno...

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Detalles Bibliográficos
Autores principales: Ali, Rahman, Hussain, Jamil, Siddiqi, Muhammad Hameed, Hussain, Maqbool, Lee, Sungyoung
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4541861/
https://www.ncbi.nlm.nih.gov/pubmed/26151207
http://dx.doi.org/10.3390/s150715921
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author Ali, Rahman
Hussain, Jamil
Siddiqi, Muhammad Hameed
Hussain, Maqbool
Lee, Sungyoung
author_facet Ali, Rahman
Hussain, Jamil
Siddiqi, Muhammad Hameed
Hussain, Maqbool
Lee, Sungyoung
author_sort Ali, Rahman
collection PubMed
description 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.
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spelling pubmed-45418612015-08-26 H2RM: A Hybrid Rough Set Reasoning Model for Prediction and Management of Diabetes Mellitus Ali, Rahman Hussain, Jamil Siddiqi, Muhammad Hameed Hussain, Maqbool Lee, Sungyoung Sensors (Basel) Article 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. MDPI 2015-07-03 /pmc/articles/PMC4541861/ /pubmed/26151207 http://dx.doi.org/10.3390/s150715921 Text en © 2015 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Ali, Rahman
Hussain, Jamil
Siddiqi, Muhammad Hameed
Hussain, Maqbool
Lee, Sungyoung
H2RM: A Hybrid Rough Set Reasoning Model for Prediction and Management of Diabetes Mellitus
title H2RM: A Hybrid Rough Set Reasoning Model for Prediction and Management of Diabetes Mellitus
title_full H2RM: A Hybrid Rough Set Reasoning Model for Prediction and Management of Diabetes Mellitus
title_fullStr H2RM: A Hybrid Rough Set Reasoning Model for Prediction and Management of Diabetes Mellitus
title_full_unstemmed H2RM: A Hybrid Rough Set Reasoning Model for Prediction and Management of Diabetes Mellitus
title_short H2RM: A Hybrid Rough Set Reasoning Model for Prediction and Management of Diabetes Mellitus
title_sort h2rm: a hybrid rough set reasoning model for prediction and management of diabetes mellitus
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4541861/
https://www.ncbi.nlm.nih.gov/pubmed/26151207
http://dx.doi.org/10.3390/s150715921
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