Multiple comparison procedures
Multiple comparison procedures
C4.5: programs for machine learning
C4.5: programs for machine learning
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Logistic regression and artificial neural network classification models: a methodology review
Journal of Biomedical Informatics
Speeding up backpropagation using multiobjective evolutionary algorithms
Neural Computation
Data mining in metric space: an empirical analysis of supervised learning performance criteria
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Machine Learning
An introduction to ROC analysis
Pattern Recognition Letters - Special issue: ROC analysis in pattern recognition
Logical Foundations for Rule-Based Systems (Studies in Computational Intelligence) (Studies in Computational Intelligence)
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)
Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)
Multiobjective optimization using variable complexity modelling for control system design
Applied Soft Computing
Neuro-genetic non-invasive temperature estimation: Intensity and spatial prediction
Artificial Intelligence in Medicine
HAIS '09 Proceedings of the 4th International Conference on Hybrid Artificial Intelligence Systems
A machine learning-based approach to prognostic analysis of thoracic transplantations
Artificial Intelligence in Medicine
Sensitivity versus accuracy in multiclass problems using memetic Pareto evolutionary neural networks
IEEE Transactions on Neural Networks
Decision support in heart failure through processing of electro- and echocardiograms
Artificial Intelligence in Medicine
Multi-objective pairwise RNA sequence alignment
Bioinformatics
Computer-aided small bowel tumor detection for capsule endoscopy
Artificial Intelligence in Medicine
A two-stage evolutionary algorithm based on sensitivity and accuracy for multi-class problems
Information Sciences: an International Journal
Neural networks for classification: a survey
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Pareto-Based Multiobjective Machine Learning: An Overview and Case Studies
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Hybridization of evolutionary algorithms and local search by means of a clustering method
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
An evolutionary artificial neural networks approach for breast cancer diagnosis
Artificial Intelligence in Medicine
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Objective: The optimal allocation of organs in liver transplantation is a problem that can be resolved using machine-learning techniques. Classical methods of allocation included the assignment of an organ to the first patient on the waiting list without taking into account the characteristics of the donor and/or recipient. In this study, characteristics of the donor, recipient and transplant organ were used to determine graft survival. We utilised a dataset of liver transplants collected by eleven Spanish hospitals that provides data on the survival of patients three months after their operations. Methods and material: To address the problem of organ allocation, the memetic Pareto evolutionary non-dominated sorting genetic algorithm 2 (MPENSGA2 algorithm), a multi-objective evolutionary algorithm, was used to train radial basis function neural networks, where accuracy was the measure used to evaluate model performance, along with the minimum sensitivity measurement. The neural network models obtained from the Pareto fronts were used to develop a rule-based system. This system will help medical experts allocate organs. Results: The models obtained with the MPENSGA2 algorithm generally yielded competitive results for all performance metrics considered in this work, namely the correct classification rate (C), minimum sensitivity (MS), area under the receiver operating characteristic curve (AUC), root mean squared error (RMSE) and Cohen's kappa (Kappa). In general, the multi-objective evolutionary algorithm demonstrated a better performance than the mono-objective algorithm, especially with regard to the MS extreme of the Pareto front, which yielded the best values of MS (48.98) and AUC (0.5659). The rule-based system efficiently complements the current allocation system (model for end-stage liver disease, MELD) based on the principles of efficiency and equity. This complementary effect occurred in 55% of the cases used in the simulation. The proposed rule-based system minimises the prediction probability error produced by two sets of models (one of them formed by models guided by one of the objectives (entropy) and the other composed of models guided by the other objective (MS)), such that it maximises the probability of success in liver transplants, with success based on graft survival three months post-transplant. Conclusion: The proposed rule-based system is objective, because it does not involve medical experts (the expert's decision may be biased by several factors, such as his/her state of mind or familiarity with the patient). This system is a useful tool that aids medical experts in the allocation of organs; however, the final allocation decision must be made by an expert.