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

Ali R, Hussain J, Siddiqi MH, Hussain M, Lee S - Sensors (Basel) (2015)

Bottom Line: 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.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 Article: PubMed Central - PubMed

Affiliation: Department of Computer Engineering, Kyung Hee University, Seocheon-dong, Giheung-gu Yongin-si, Gyeonggi-do 446-701, Korea. rahmanali@oslab.khu.ac.kr.

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.

No MeSH data available.


Related in: MedlinePlus

An encounter of type-2 diabetes mellitus patient (T2DM), following subjective, objective, assessment, and plan (SOAP)-based protocol.
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sensors-15-15921-f002: An encounter of type-2 diabetes mellitus patient (T2DM), following subjective, objective, assessment, and plan (SOAP)-based protocol.

Mentions: Data from 50 diabetes patients, 20 with type-1 and 30 type-2 DM is acquired from a local hospital that records patients observations in clinical charts, following SOAP (Subjective, Objective, Assessment and Plan)-based protocol [35]. In the hospital, data are collected over the period of four years from 2008 to 2011 with an average of eight encounters per patient. The minimum number of encounters recorded for a patient is two and the maximum is eighteen. In the charts, patient information containing physiological data, clinical laboratory tests findings, diagnosis information and recommendations are recorded in Subjectivity, Objectivity, Assessment and Planning sections. In all the charts, Subjectivity and Objectivity sections are merged in one section, titled S & O. The Assessment section is put at the top of each encounter and sometime before the Planning section. Different encounters with the same patients are recorded in the same chart to maintain their history in one document. An example of an encounter with a T2DM patient’s chart is shown in Figure 2.


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

Ali R, Hussain J, Siddiqi MH, Hussain M, Lee S - Sensors (Basel) (2015)

An encounter of type-2 diabetes mellitus patient (T2DM), following subjective, objective, assessment, and plan (SOAP)-based protocol.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4541861&req=5

sensors-15-15921-f002: An encounter of type-2 diabetes mellitus patient (T2DM), following subjective, objective, assessment, and plan (SOAP)-based protocol.
Mentions: Data from 50 diabetes patients, 20 with type-1 and 30 type-2 DM is acquired from a local hospital that records patients observations in clinical charts, following SOAP (Subjective, Objective, Assessment and Plan)-based protocol [35]. In the hospital, data are collected over the period of four years from 2008 to 2011 with an average of eight encounters per patient. The minimum number of encounters recorded for a patient is two and the maximum is eighteen. In the charts, patient information containing physiological data, clinical laboratory tests findings, diagnosis information and recommendations are recorded in Subjectivity, Objectivity, Assessment and Planning sections. In all the charts, Subjectivity and Objectivity sections are merged in one section, titled S & O. The Assessment section is put at the top of each encounter and sometime before the Planning section. Different encounters with the same patients are recorded in the same chart to maintain their history in one document. An example of an encounter with a T2DM patient’s chart is shown in Figure 2.

Bottom Line: 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.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 Article: PubMed Central - PubMed

Affiliation: Department of Computer Engineering, Kyung Hee University, Seocheon-dong, Giheung-gu Yongin-si, Gyeonggi-do 446-701, Korea. rahmanali@oslab.khu.ac.kr.

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.

No MeSH data available.


Related in: MedlinePlus