Vague Rough Set Techniques for Uncertainty Processing in Relational Database Model

Singh, Karan; Thakur, Santosh Singh; Lal, Mangi

doi:10.15388/informatica.2008.205

Cited by 8 publications

(3 citation statements)

References 22 publications

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“…So, hybrid approach which used in the INDBS can be suitable in making treatment decisions. Both of Vague rough set model [23] and intuitionistic fuzzy rough technique [24] can be utilized as alternative techniques for RST in the developed versions.…”

Section: Discussionmentioning

confidence: 99%

Development of an Intelligent Approach for Medical Knowledge Discovery and Decision Support

Sakr¹,

Mosa²,

Shehabeldien³

2014

IJCA

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Electronic knowledgebase (including Electronic Medical Record), together with inference procedures, form an intelligent medical information systems that offers many possibilities for health care providers. It acts as a strong base for scientific upgrading and provides an enormous support for developing new medical decisions. This paper proposes an intelligent neurosurgical decision support system framework. This framework merges the advantages of electronic medical record, rough set theory, clinical pathways, latest available scientific researches, and patient's expectations. We have designed and developed this aiming to get a support system for predicting the ideal treatment method of lumbar disc patients, in addition to evaluating the treatment plan. The system ensures future up-to-date knowledgebase, through permitting upgrading with the most recent innovations in knowledge and discoveries. This framework is expected to improve the quality of health care by providing the necessary requirements of neurosurgery domain.

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Section: Discussionmentioning

confidence: 99%

Development of an Intelligent Approach for Medical Knowledge Discovery and Decision Support

Sakr¹,

Mosa²,

Shehabeldien³

2014

IJCA

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“…We mine prediction rules from the diabetes data using techniques supported by RST. Our choice of RST is due to its powerful nature of analyzing and handling vague and uncertain information in classification problems [ 50 ]. RST uses a formalism that represents and analyses data in its specific format that is described in a structured form called information system, therefore we named our dataset DMIS.…”

Section: Methodsmentioning

confidence: 99%

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

Ali

Hussain

Ṣiddiqi

et al. 2015

Sensors

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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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“…Rough set theory, introduced by Pawlak [1], is a kind of important theory about uncertainty information processing. So far, it has been successfully applied in data analysis, pattern recognition, machine learning and knowledge discovery, artificial intelligence, and so on [2][3][4][5][6][7]. Using the tool of entropy to deal with the uncertainty problem in rough set theory has been already studied [8][9][10][11], and the combination entropy was proposed in the literature [12].…”

Section: Introductionmentioning

confidence: 99%

Three-way weighted combination-entropies based on three-layer granular structures

Wang

Tang

Zhang

et al. 2017

Applied Mathematics and Nonlinear Sciences

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Rough set theory is an important theory for the uncertain information processing. The information theoretic measures have been introduced into rough set theory and provided a new effective method in uncertainty measurement and attribute reduction. However, most of them did not consider the hierarchical structure of a decision table (D-Table). Thus, this paper concretely constructs three-way weighted combination-entropies based on the D-Table’s three-layer granular structures and Bayes’ theorem from a new perspective, and reveals the granulation monotonicity and systematic relationships of three-way weighted combination-entropies. The relevant conclusion provides a more complete and updated interpretation of granular computing for the uncertainty measurement, and it also establishes a more effective basis for the quantitative application in attribute reduction.

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Vague Rough Set Techniques for Uncertainty Processing in Relational Database Model

Cited by 8 publications

References 22 publications

Development of an Intelligent Approach for Medical Knowledge Discovery and Decision Support

Development of an Intelligent Approach for Medical Knowledge Discovery and Decision Support

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

Three-way weighted combination-entropies based on three-layer granular structures

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