Abstract
An interval type-2 fuzzy logic system is introduced for cancer diagnosis using mass spectrometry-based proteomic data. The fuzzy system is incorporated with a feature extraction procedure that combines wavelet transform and Wilcoxon ranking test. The proposed feature extraction generates feature sets that serve as inputs to the type-2 fuzzy classifier. Uncertainty, noise and outliers that are common in the proteomic data motivate the use of type-2 fuzzy system. Tabu search is applied for structure learning of the fuzzy classifier. Experiments are performed using two benchmark proteomic datasets for the prediction of ovarian and pancreatic cancer. The dominance of the suggested feature extraction as well as type-2 fuzzy classifier against their competing methods is showcased through experimental results. The proposed approach therefore is helpful to clinicians and practitioners as it can be implemented as a medical decision support system in practice.
Original language | English |
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Title of host publication | FUZZ-IEEE 2015 - IEEE International Conference on Fuzzy Systems |
Editors | Adnan Yazici, Nikhil R. Pal, Hisao Ishibuchi, Bulent Tutmez, Chin-Teng Lin, Joao M. C. Sousa, Uzay Kaymak, Trevor Martin |
Publisher | IEEE, Institute of Electrical and Electronics Engineers |
ISBN (Electronic) | 9781467374286 |
DOIs | |
Publication status | Published - 2015 |
Externally published | Yes |
Event | IEEE International Conference on Fuzzy Systems 2015 - Istanbul, Türkiye Duration: 2 Aug 2015 → 5 Aug 2015 https://ieeexplore.ieee.org/xpl/conhome/7329077/proceeding |
Conference
Conference | IEEE International Conference on Fuzzy Systems 2015 |
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Abbreviated title | FUZZ-IEEE 2015 |
Country/Territory | Türkiye |
City | Istanbul |
Period | 2/08/15 → 5/08/15 |
Internet address |
Keywords
- Cancer diagnosis
- interval type-2 fuzzy logic system
- mass spectrometry
- tabu search
- wavelet transform
- Wilcoxon test