What are fuzzy rules and how to use them
Fuzzy Sets and Systems - Special issue dedicated to the memory of Professor Arnold Kaufmann
Using soft computing to build real world intelligent decision support systems in uncertain domains
Decision Support Systems - Special issue on decision support in the new millennium
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Three objective genetics-based machine learning for linguisitc rule extraction
Information Sciences: an International Journal - Recent advances in genetic fuzzy systems
Controlled Elitist Non-dominated Sorting Genetic Algorithms for Better Convergence
EMO '01 Proceedings of the First International Conference on Evolutionary Multi-Criterion Optimization
Evolutionary Training of a Neurofuzzy Network for Detection of P Wave of the ECG
ICCIMA '99 Proceedings of the 3rd International Conference on Computational Intelligence and Multimedia Applications
ECG beat classification using neuro-fuzzy network
Pattern Recognition Letters
Multiobjective Evolutionary Algorithms: Analyzing the State-of-the-Art
Evolutionary Computation
Application of Fuzzy Logic to Approximate Reasoning Using Linguistic Synthesis
IEEE Transactions on Computers
Muiltiobjective optimization using nondominated sorting in genetic algorithms
Evolutionary Computation
Environmental Modelling & Software
A study of cross-validation and bootstrap for accuracy estimation and model selection
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
A fuzzy clustering neural network architecture for classification of ECG arrhythmias
Computers in Biology and Medicine
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
A fuzzy approach to computer-assisted myocardial ischemia diagnosis
Artificial Intelligence in Medicine
Design of a Fuzzy-based Decision Support System for Coronary Heart Disease Diagnosis
Journal of Medical Systems
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This paper aims at identifying the factors that would help to diagnose acute myocardial infarction (AMI) using data from an electronic medical record system (EMR) and then generating structure decisions in the form of linguistic fuzzy rules to help predict and understand the outcome of the diagnosis. Since there is a tradeoff in the fuzzy system between the accuracy which measures the capability of the system to predict the diagnosis of AMI and transparency which reflects its ability to describe the symptoms-diagnosis relation in an understandable way, the proposed fuzzy rules are designed in a such a way to find an appropriate balance between these two conflicting modeling objectives using multi-objective genetic algorithms. The main advantage of the generated linguistic fuzzy rules is their ability to describe the relation between the symptoms and the outcome of the diagnosis in an understandable way, close to human thinking and this feature may help doctors to understand the decision process of the fuzzy rules.