Tissue classification with gene expression profiles
RECOMB '00 Proceedings of the fourth annual international conference on Computational molecular biology
Bayesian Networks in Ovarian Cancer Diagnosis: Potentials and Limitations
CBMS '00 Proceedings of the 13th IEEE Symposium on Computer-Based Medical Systems (CBMS'00)
An Application of Artificial Neural Networks in Ovarian Cancer Early Detection
IJCNN '00 Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 4 - Volume 4
Neural Networks - 2005 Special issue: IJCNN 2005
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Ovarian cancer prognosis by hemostasis and complementary learning
ICONIP'06 Proceedings of the 13th international conference on Neural information processing - Volume Part III
Falcon: neural fuzzy control and decision systems using FKP and PFKP clustering algorithms
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Using literature and data to learn Bayesian networks as clinical models of ovarian tumors
Artificial Intelligence in Medicine
MCES: A Novel Monte Carlo Evaluative Selection Approach for Objective Feature Selections
IEEE Transactions on Neural Networks
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
ICAISC'12 Proceedings of the 11th international conference on Artificial Intelligence and Soft Computing - Volume Part II
Automated Diagnosis Through Ontologies and Logical Descriptions: The ADONIS Approach
International Journal of Decision Support System Technology
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Objective: Early detection is paramount to reduce the high death rate of ovarian cancer. Unfortunately, current detection tool is not sensitive. New techniques such as deoxyribonucleic acid (DNA) micro-array and proteomics data are difficult to analyze due to high dimensionality, whereas conventional methods such as blood test are neither sensitive nor specific. Methods: Thus, a functional model of human pattern recognition known as complementary learning fuzzy neural network (CLFNN) is proposed to aid existing diagnosis methods. In contrast to conventional computational intelligence methods, CLFNN exploits the lateral inhibition between positive and negative samples. Moreover, it is equipped with autonomous rule generation facility. An example named fuzzy adaptive learning control network with another adaptive resonance theory (FALCON-AART) is used to illustrate the performance of CLFNN. Results: The confluence of CLFNN-micro-array, CLFNN-blood test, and CLFNN-proteomics demonstrate good sensitivity and specificity in the experiments. The diagnosis decision is accurate and consistent. CLFNN also outperforms most of the conventional methods. Conclusions: This research work demonstrates that the confluence of CLFNN-DNA micro-array, CLFNN-blood tests, and CLFNN-proteomic test improves the diagnosis accuracy with higher consistency. CLFNN exhibits good performance in ovarian cancer diagnosis in general. Thus, CLFNN is a promising tool for clinical decision support.