Neuro-fuzzy classification of prostate cancer using NEFCLASS-J

Authors:
Ayturk Keles;A. Samet Hasiloglu;Ali Keles;Yilmaz Aksoy
Affiliations:
Department of Computer Engineering, Faculty of Engineering, Ataturk University, 25240-Erzurum, Turkey;Department of Computer Engineering, Faculty of Engineering, Ataturk University, 25240-Erzurum, Turkey;Department of Computer Education and Instructional Technology, Faculty of Kazim Karabekir Education, Ataturk University, 25240-Erzurum, Turkey;Department of Urology, Medical Faculty, Ataturk University, 25240-Erzurum, Turkey
Venue:
Computers in Biology and Medicine
Year:
2007

Citing 6
Cited 9

Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence

Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
NEFCLASSmdash;a neuro-fuzzy approach for the classification of data

SAC '95 Proceedings of the 1995 ACM symposium on Applied computing
Foundations of Neuro-Fuzzy Systems

Foundations of Neuro-Fuzzy Systems
Intelligent Systems and Soft Computing: Prospects, Tools and Applications

Intelligent Systems and Soft Computing: Prospects, Tools and Applications
A fuzzy expert system design for diagnosis of prostate cancer

CompSysTech '03 Proceedings of the 4th international conference conference on Computer systems and technologies: e-Learning
Fuzzy decision trees: issues and methods

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics

Medical data mining by fuzzy modeling with selected features

Artificial Intelligence in Medicine
Incremental local linear fuzzy classifier in fisher space

EURASIP Journal on Advances in Signal Processing
Expert system based on neuro-fuzzy rules for diagnosis breast cancer

Expert Systems with Applications: An International Journal
Diagnosis of hypoglycemic episodes using a neural network based rule discovery system

Expert Systems with Applications: An International Journal
Prediction of nasopharyngeal carcinoma recurrence by neuro-fuzzy techniques

Fuzzy Sets and Systems
Natural occurrence of nocturnal hypoglycemia detection using hybrid particle swarm optimized fuzzy reasoning model

Artificial Intelligence in Medicine
Fuzzy expert system for predicting pathological stage of prostate cancer

Expert Systems with Applications: An International Journal
A hybrid fuzzy-ontology based intelligent system to determine level of severity and treatment recommendation for Benign Prostatic Hyperplasia

Computer Methods and Programs in Biomedicine
A neuro-fuzzy approach in the classification of students' academic performance

Computational Intelligence and Neuroscience

Quantified Score

Hi-index	0.00

Visualization

Abstract

Medical diagnosis has been the most proper area for the implementations of artificial intelligence for approximately 20 years. In this paper, a new approach based on neuro-fuzzy classification (NEFCLASS) tool has been presented to classify prostate cancer. The tool has the features of batch learning, automatic cross validation, automatic determination of the rule base size, and handling of missing values to increase its interpretability. We have investigated how good medical data analysis could be done with NEFCLASS-J, and what effects selected parameters have on classifier performances. Medical data were obtained from patients with real prostate cancer and benign prostatic hyperplasia (BPH). The reason for the selection of these two illnesses was the fact that their symptoms are very similar yet their differentiation is very crucial. The results showed that, for creating high performance of classifier appropriate for the data used, firstly it is necessary to decide well on the membership type and the number of fuzzy sets and then validation procedure. After a good classifier has been found, other parameters should be investigated to improve this classifier. In the light of this study, we can present a foresight for the diagnosis of the patients with prostate cancer or BPH.