ANEMIA: An expert consultation system
Computers and Biomedical Research
A performance evaluation of the expert system ANEMIA
Computers and Biomedical Research
NEOANEMIA: a knowledge-based system emulating diagnostic reasoning
Computers and Biomedical Research
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations
Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Genetic programming using a minimum description length principle
Advances in genetic programming
An expert system to diagnose anemia and report results directly on hematology forms
Computers and Biomedical Research
Neuro-fuzzy approach versus rough-set inspired methodology for intelligent decision support
Information Sciences—Informatics and Computer Science: An International Journal
Genetic programming based pattern classification with feature space partitioning
Information Sciences: an International Journal
Genetic programming for model selection of TSK-fuzzy systems
Information Sciences: an International Journal - Recent advances in genetic fuzzy systems
Combining GP operators with SA search to evolve fuzzy rule based classifiers
Information Sciences: an International Journal - Recent advances in genetic fuzzy systems
A new hybrid case-based architecture for medical diagnosis
Information Sciences—Informatics and Computer Science: An International Journal
Overfitting avoidance in genetic programming of polynomials
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
A comparison of classification accuracy of four genetic programming-evolved intelligent structures
Information Sciences: an International Journal
Regularization approach to inductive genetic programming
IEEE Transactions on Evolutionary Computation
Population variation in genetic programming
Information Sciences: an International Journal
Designing of classifiers based on immune principles and fuzzy rules
Information Sciences: an International Journal
pth moment stability analysis of stochastic recurrent neural networks with time-varying delays
Information Sciences: an International Journal
The theoretical foundations of statistical learning theory based on fuzzy number samples
Information Sciences: an International Journal
Information Sciences: an International Journal
A non-symbolic implementation of abdominal pain estimation in childhood
Information Sciences: an International Journal
Automatic identification of cardiac health using modeling techniques: A comparative study
Information Sciences: an International Journal
Dynamic population variation in genetic programming
Information Sciences: an International Journal
Information Sciences: an International Journal
A survey on the application of genetic programming to classification
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Information Sciences: an International Journal
G3P-MI: A genetic programming algorithm for multiple instance learning
Information Sciences: an International Journal
Comparing performances of backpropagation and genetic algorithms in the data classification
Expert Systems with Applications: An International Journal
A relevance feedback method based on genetic programming for classification of remote sensing images
Information Sciences: an International Journal
Multi-stage genetic programming: A new strategy to nonlinear system modeling
Information Sciences: an International Journal
Information Sciences: an International Journal
Predicting asthma outcome using partial least square regression and artificial neural networks
Advances in Artificial Intelligence
A two-layered classifier based on the radial basis function for the screening of thalassaemia
Computers in Biology and Medicine
Review: Knowledge discovery in medicine: Current issue and future trend
Expert Systems with Applications: An International Journal
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This paper presents the use of a neural network and a decision tree, which is evolved by genetic programming (GP), in thalassaemia classification. The aim is to differentiate between thalassaemic patients, persons with thalassaemia trait and normal subjects by inspecting characteristics of red blood cells, reticulocytes and platelets. A structured representation on genetic algorithms for non-linear function fitting or STROGANOFF is the chosen architecture for genetic programming implementation. For comparison, multilayer perceptrons are explored in classification via a neural network. The classification results indicate that the performance of the GP-based decision tree is approximately equal to that of the multilayer perceptron with one hidden layer. But the multilayer perceptron with two hidden layers, which is proven to have the most suitable architecture among networks with different number of hidden layers, outperforms the GP-based decision tree. Nonetheless, the structure of the decision tree reveals that some input features have no effects on the classification performance. The results confirm that the classification accuracy of the multilayer perceptron with two hidden layers can still be maintained after the removal of the redundant input features. Detailed analysis of the classification errors of the multilayer perceptron with two hidden layers, in which a reduced feature set is used as the network input, is also included. The analysis reveals that the classification ambiguity and misclassification among persons with minor thalassaemia trait and normal subjects is the main cause of classification errors. These results suggest that a combination of a multilayer perceptron with a blood cell analysis may give rise to a guideline/hint for further investigation of thalassaemia classification.