Bayesian network construction using a fuzzy rule based approach for medical decision support

2014 ◽  
Vol 32 (3) ◽  
pp. 344-369 ◽  
Author(s):  
Vasilios Zarikas ◽  
Elpiniki Papageorgiou ◽  
Peter Regner
Keyword(s):  
Decision Support ◽  
Bayesian Network ◽  
Fuzzy Rule ◽  
Medical Decision ◽  
Rule Based ◽  
Network Construction ◽  
Medical Decision Support ◽  
Rule Based Approach

Modeling Interpretable Fuzzy Rule-Based Classifiers for Medical Decision Support

2012 ◽  
pp. 255-272 ◽  
Author(s):  
Jose M. Alonso ◽  
Ciro Castiello ◽  
Marco Lucarelli ◽  
Corrado Mencar
Keyword(s):  
Decision Support ◽  
Fuzzy Rule ◽  
Linguistic Knowledge ◽  
Medical Decision ◽  
Fuzzy Classifier ◽  
Rule Based ◽  
Medical Decision Support ◽  
Modeling Methodology ◽  
Stage Renal Disease ◽  
End Stage

Decision support systems in Medicine must be easily comprehensible, both for physicians and patients. In this chapter, the authors describe how the fuzzy modeling methodology called HILK (Highly Interpretable Linguistic Knowledge) can be applied for building highly interpretable fuzzy rule-based classifiers (FRBCs) able to provide medical decision support. As a proof of concept, they describe the case study of a real-world scenario concerning the development of an interpretable FRBC that can be used to predict the evolution of the end-stage renal disease (ESRD) in subjects affected by Immunoglobin A Nephropathy (IgAN). The designed classifier provides users with a number of rules which are easy to read and understand. The rules classify the prognosis of ESRD evolution in IgAN-affected subjects by distinguishing three classes (short, medium, long). Experimental results show that the fuzzy classifier is capable of satisfactory accuracy results – in comparison with Multi-Layer Perceptron (MLP) neural networks – and high interpretability of the knowledge base.

Modeling Interpretable Fuzzy Rule-Based Classifiers for Medical Decision Support

Data Mining ◽  
2013 ◽  
pp. 1064-1081 ◽  
Author(s):  
Jose M. Alonso ◽  
Ciro Castiello ◽  
Marco Lucarelli ◽  
Corrado Mencar
Keyword(s):  
Decision Support ◽  
Fuzzy Rule ◽  
Linguistic Knowledge ◽  
Medical Decision ◽  
Fuzzy Classifier ◽  
Rule Based ◽  
Medical Decision Support ◽  
Modeling Methodology ◽  
Stage Renal Disease ◽  
End Stage

Decision support systems in Medicine must be easily comprehensible, both for physicians and patients. In this chapter, the authors describe how the fuzzy modeling methodology called HILK (Highly Interpretable Linguistic Knowledge) can be applied for building highly interpretable fuzzy rule-based classifiers (FRBCs) able to provide medical decision support. As a proof of concept, they describe the case study of a real-world scenario concerning the development of an interpretable FRBC that can be used to predict the evolution of the end-stage renal disease (ESRD) in subjects affected by Immunoglobin A Nephropathy (IgAN). The designed classifier provides users with a number of rules which are easy to read and understand. The rules classify the prognosis of ESRD evolution in IgAN-affected subjects by distinguishing three classes (short, medium, long). Experimental results show that the fuzzy classifier is capable of satisfactory accuracy results – in comparison with Multi-Layer Perceptron (MLP) neural networks – and high interpretability of the knowledge base.

Rule-based Medical Decision Support Portal for the Emergency Department

2015 ◽  
pp. 640-652
Author(s):  
I-Chin Wu ◽  
Tzu-Li Chen ◽  
Yen-Yi Feng ◽  
Ya-Ling Cheng ◽  
Yung-Chih Chuang
Keyword(s):  
Emergency Department ◽  
Decision Support ◽  
Medical Decision ◽  
Rule Based ◽  
Medical Decision Support

MRI Brain Tumor Image Analysis Using Fuzzy Rule based Approach

2019 ◽  
Vol 50 (2) ◽  
pp. 98-112 ◽  
Author(s):  
KALYAN KUMAR JENA ◽  
SASMITA MISHRA ◽  
SAROJANANDA MISHRA ◽  
SOURAV KUMAR BHOI ◽  
SOUMYA RANJAN NAYAK
Keyword(s):  
Image Analysis ◽  
Brain Tumor ◽  
Fuzzy Rule ◽  
Rule Based ◽  
Mri Brain ◽  
Rule Based Approach
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