Fuzzy Case-based Reasoning System Research Articles

A case-base fuzzification process: diabetes diagnosis case study

Medical case-based reasoning (CBR) systems require the handling of vague or imprecise data. The fuzzy set theory is particularly suitable for this purpose. This paper proposes a case-base preparation framework for CBR systems, which converts the electronic health record medical data into fuzzy CBR knowledge. It generates fuzzy case-base knowledge by suggesting a standard crisp entity–relationship data model for CBR case-base. The resulting data model is fuzzified using a proposed relational data model fuzzification methodology. The performances of this methodology and its resulting fuzzy case-base structure are evaluated. Diabetes diagnosis is used as a case study. A set of 60 real diabetic cases is used in the study. A fuzzy CBR system is implemented to check the diagnoses accuracy. It combines the resulting fuzzy case-base with a proposed fuzzy similarity measure. Experimental results indicate that the proposed fuzzy CBR method is superior to traditional CBR and other machine-learning methods. Our fuzzy CBR achieves an accuracy of 95%, a precision of 96%, a recall 97.96%, an f-measure of 96.97%, a specificity of 81.82%, and good robustness for dealing with vagueness. The resulting fuzzy case-base relational database enhances the representation of case-base knowledge, the performance of retrieval algorithms, and the querying capabilities of CBR systems.

Fuzzy Case-Based Reasoning System

In this paper, we propose a fuzzy case-based reasoning system, using a case-based reasoning (CBR) system that learns from experience to solve problems. Different from a traditional case-based reasoning system that uses crisp cases, our system works with fuzzy ones. Specifically, we change a crisp case into a fuzzy one by fuzzifying each crisp case element (feature), according to the maximum degree principle. Thus, we add the “vague” concept into a case-based reasoning system. It is these somewhat vague inputs that make the outcomes of the prediction more meaningful and accurate, which illustrates that it is not necessarily helpful when we always create accurate predictive relations through crisp cases. Finally, we prove this and apply this model to practical weather forecasting, and experiments show that using fuzzy cases can make some prediction results more accurate than using crisp cases.

Designing and Implementation of Fuzzy Case-based Reasoning System on Android Platform Using Electronic Discharge Summary of Patients with Chronic Kidney Diseases.

Introduction:Case-based reasoning (CBR) systems are one of the effective methods to find the nearest solution to the current problems. These systems are used in various spheres as well as industry, business, and economy. The medical field is not an exception in this regard, and these systems are nowadays used in the various aspects of diagnosis and treatment.Methodology:In this study, the effective parameters were first extracted from the structured discharge summary prepared for patients with chronic kidney diseases based on data mining method. Then, through holding a meeting with experts in nephrology and using data mining methods, the weights of the parameters were extracted. Finally, fuzzy system has been employed in order to compare the similarities of current case and previous cases, and the system was implemented on the Android platform.Discussion:The data on electronic discharge records of patients with chronic kidney diseases were entered into the system. The measure of similarity was assessed using the algorithm provided in the system, and then compared with other known methods in CBR systems.Conclusion:Developing Clinical fuzzy CBR system used in Knowledge management framework for registering specific therapeutic methods, Knowledge sharing environment for experts in a specific domain and Powerful tools at the point of care.

Cognitive amalgam with a distributed fuzzy case-based reasoning system for an accurate cardiac arrhythmias diagnosis

The distributed case-based reasoning consists of distributing the reasoning through a set of agents and the cases through a set of case bases for improving the performance and for ensuring the maintainability of the reasoning system. The most important challenge of the medical applications resides in the precision of decisions; hence the novel decision support systems should introduce more aspects of reasoning for achieving this criterion. In this paper, we describe a cognitive amalgam with a distributed case-based reasoning enriched by an expert system which infer from an XML knowledge base extracted from the domain expertise. Each cognitive agent capitalises its knowledge from same class cases by using a machine learning algorithm enriched by a fuzzy similarity measures function. The aim of this proposition is to reach the optimal criteria of medical applications. The achieved experimental results applied in the diagnosis of complex type of data obtained from the electrocardiogram ECG signal improves our proposition and promotes more realisations in the medical domain.

Reducing the Memory Size of a Fuzzy Case-Based Reasoning System Applying Rough Set Techniques

Early work on case-based reasoning (CBR) reported in the literature shows the importance of soft computing techniques applied to different stages of the classical four-step CBR life cycle. This correspondence proposes a reduction technique based on rough sets theory capable of minimizing the case memory by analyzing the contribution of each case feature. Inspired by the application of the minimum description length principle, the method uses the granularity of the original data to compute the relevance of each attribute. The rough feature weighting and selection method is applied as a preprocessing step prior to the generation of a fuzzy rule system, which is employed in the revision phase of the proposed CBR system. Experiments using real oceanographic data show that the rough sets reduction method maintains the accuracy of the employed fuzzy rules, while reducing the computational effort needed in its generation and increasing the explanatory strength of the fuzzy rules