Fuzzy logic alternative for analysis in the biomedical sciences
Computers and Biomedical Research
A Mixture of Experts Network Structure for Breast Cancer Diagnosis
Journal of Medical Systems
Combining Neural Network Models for Automated Diagnostic Systems
Journal of Medical Systems
Expert Systems with Applications: An International Journal
Usage of eigenvector methods to improve reliable classifier for Doppler ultrasound signals
Computers in Biology and Medicine
A novel large-memory neural network as an aid in medical diagnosis applications
IEEE Transactions on Information Technology in Biomedicine
Artificial Intelligence in Medicine
A combined neural network and decision trees model for prognosis of breast cancer relapse
Artificial Intelligence in Medicine
Model selection for a medical diagnostic decision support system: a breast cancer detection case
Artificial Intelligence in Medicine
Input feature selection for classification problems
IEEE Transactions on Neural Networks
IVIC '09 Proceedings of the 1st International Visual Informatics Conference on Visual Informatics: Bridging Research and Practice
Journal of Medical Systems
Detection of Resistivity for Antibiotics by Probabilistic Neural Networks
Journal of Medical Systems
A Software Tool for Determination of Breast Cancer Treatment Methods Using Data Mining Approach
Journal of Medical Systems
A Neuro-Fuzzy Identification of ECG Beats
Journal of Medical Systems
Classification of respiratory abnormalities using adaptive neuro fuzzy inference system
ACIIDS'12 Proceedings of the 4th Asian conference on Intelligent Information and Database Systems - Volume Part III
Tool wear monitoring using neuro-fuzzy techniques: a comparative study in a turning process
Journal of Intelligent Manufacturing
Breast Cancer Classification Based on Advanced Multi Dimensional Fuzzy Neural Network
Journal of Medical Systems
Optimizing the modified fuzzy ant-miner for efficient medical diagnosis
Applied Intelligence
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This paper intends to an integrated view of implementing adaptive neuro-fuzzy inference system (ANFIS) for breast cancer detection. The Wisconsin breast cancer database contained records of patients with known diagnosis. The ANFIS classifiers learned how to differentiate a new case in the domain by given a training set of such records. The ANFIS classifier was used to detect the breast cancer when nine features defining breast cancer indications were used as inputs. The proposed ANFIS model combined the neural network adaptive capabilities and the fuzzy logic qualitative approach. Some conclusions concerning the impacts of features on the detection of breast cancer were obtained through analysis of the ANFIS. The performance of the ANFIS model was evaluated in terms of training performances and classification accuracies and the results confirmed that the proposed ANFIS model has potential in detecting the breast cancer.