Design of intelligent system for medical applications using rough set theory

Tiwari, Kanchan; Kothari, Ashwin

doi:10.1504/ijdmmm.2016.079069

Cited by 3 publications

(3 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Real applications of the FPGA-based rough set method data processing systems are described in [41,42]. In [41], the rough set coprocessor is described.…”

Section: Hardware Approaches For Finding Reductsmentioning

confidence: 99%

See 1 more Smart Citation

Breadth search strategies for finding minimal reducts: towards hardware implementation

Choromański

Grzes

Hońko

2020

Neural Comput & Applic

View full text Add to dashboard Cite

Attribute reduction, being a complex problem in data mining, has attracted many researchers. The importance of this issue rises due to ever-growing data to be mined. Together with data growth, a need for speeding up computations increases. The contribution of this paper is twofold: (1) investigation of breadth search strategies for finding minimal reducts in order to emerge the most promising method for processing large data sets; (2) development and implementation of the first hardware approach to finding minimal reducts in order to speed up time-consuming computations. Experimental research showed that for software implementation blind breadth search strategy is in general faster than frequency-based breadth search strategy not only in finding all minimal reducts but also in finding one of them. An inverse situation was observed for hardware implementation. In the future work, the implemented tool is to be used as a fundamental module in a system to be built for processing large data sets.Keywords Attribute reduction Á Minimal reducts Á Breadth search strategy Á Hardware implementation Á Field programmable gate arrays Neural Computing and Applications https://doi.org/10.1007/s00521-020-04833-7( 0123456789().,-volV) (0123456789(). ,-volV)Publisher's Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Neural Computing and Applications

show abstract

“…Real applications of the FPGA-based rough set method data processing systems are described in [41,42]. In [41], the rough set coprocessor is described.…”

Section: Hardware Approaches For Finding Reductsmentioning

confidence: 99%

“…For the reduct generation, there is a discernibility matrix used. In [42], there is an intelligent system for medical applications described. The system was tested for breast cancer detection and achieved a high acceleration compared to the general-purpose processor even though the much lower clock frequency.…”

Section: Hardware Approaches For Finding Reductsmentioning

confidence: 99%

Breadth search strategies for finding minimal reducts: towards hardware implementation

Choromański

Grzes

Hońko

2020

Neural Comput & Applic

View full text Add to dashboard Cite

show abstract

“…Many researchers have successfully used rough, grey and fuzzy sets in the field of medical diagnosis and medical data analysis (Kumar and Inbarani, 2015; Rezvan et al , 2014; Tiwari and Kothari, 2016). Nazari et al used the combination of the fuzzy analytic hierarchy process (FAHP) and the fuzzy inference system (FIS) to develop an expert system for the diagnosis of heart diseases (Nazari et al , 2018).…”

Section: Introductionmentioning

confidence: 99%

Designing a medical rule model system by using rough–grey modeling

Karimi

Hojati

2020

View full text Add to dashboard Cite

PurposeIn this study, a hybrid rough and grey set-based rule model is designed for diagnosis of one type of blood cancer called multiple myeloma (MM). The grey clustering method is used to combine the same condition attributes and to improve the validity of the final model.Design/methodology/approachSome tools of the rough set theory (RST) and grey incidence analysis (GIA) are used in this research to analyze the serum protein electrophoresis (SPE) test results. An RST-based rule model is extracted based on the laboratory SPE test results of patients. Also, one decision attribute and 15 condition attributes are used to extract the rules. About four rule models are constructed due to the different algorithms of data complement, discretization, reduction and rule generation. In the following phases, the condition attributes are clustered into seven clusters by using a grey clustering method, the value set of the decision attribute is decreased by using manual discretizing and the number of observations is increased in order to improve the accuracy of the model. Cross-validation is used for evaluation of the model results and finally, the best model is chosen with 5,216 rules and 98% accuracy.FindingsIn this paper, a new rule model with high accuracy is extracted based on the combination of the grey clustering method and RST modeling for diagnosis of the MM disease. Also, four primary rule models and four improved rule models have been extracted from different decision tables in order to define the result of SPE test of patients. The maximum average accuracy of improved models is equal to 95% and related to the gamma globulins percentage attribute/object-related reducts (GA/ORR) model.Research limitations/implicationsThe total number of observations for rule extraction is 115 and the results can be improved by further samples. To make the designed expert system handy in the laboratory, new computer software is under construction to import data automatically from the electrophoresis machine into the resultant rule model system.Originality/valueThe main originality of this paper is to use the RST and GST together to design and create a hybrid rule model to diagnose MM. Although many studies have been carried out on designing expert systems in medicine and cancer diagnosis, no studies have been found in designing systems to diagnose MM. On the other hand, using the grey clustering method for combining the condition attributes is a novel solution for improving the accuracy of the rule model.

show abstract

FPGA supported rough set reduct calculation for big datasets

Kopczynski

Grzes²

2022

J Intell Inf Syst

View full text Add to dashboard Cite

Design of intelligent system for medical applications using rough set theory

Cited by 3 publications

References 0 publications

Breadth search strategies for finding minimal reducts: towards hardware implementation

Breadth search strategies for finding minimal reducts: towards hardware implementation

Designing a medical rule model system by using rough–grey modeling

FPGA supported rough set reduct calculation for big datasets

Contact Info

Product

Resources

About