Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/89612
Title: Ovarian cancer diagnosis using a hybrid intelligent system with simple yet convincing rules
Authors: Ng, Geok See
Wang, Di
Chai, Quek
Keywords: Decision Support System
Ovarian Cancer Diagnosis
DRNTU::Engineering::Computer science and engineering
Issue Date: 2014
Source: Wang, D., Chai, Q. , & Ng, G. S. (2014). Ovarian cancer diagnosis using a hybrid intelligent system with simple yet convincing rules. Applied Soft Computing, 2025-39. doi:10.1016/j.asoc.2013.12.018
Series/Report no.: Applied Soft Computing
Abstract: Ovarian cancer is the ninth most common cancer among women and ranks fifth in cancer deaths. Statistics show that the five-year survival rate is greater than 75% if diagnosis occurs before the cancer cells have spread to other organs (stage I), but it drops to 20% when the cancer cells have spread to upper abdomen (stage III). Therefore, it is crucial to detect ovarian cancer as early as possible and to correctly identify the stage of the cancer to prevent any further delay of appropriate treatments. In this paper, we propose a novel self-organizing neural fuzzy inference system that functions as a reliable decision support system for ovarian cancer diagnoses. The system only requires a limited number of control parameters and constraints to derive simple yet convincing inference rules without human intervention and expert guidance. Because feature selection and attribute reduction are performed during training, the inference rules possess a great level of interpretability. Experiments are conducted on both established medical data sets and real-world cases collected from hospital. The experimental results of our proposed model in ovarian cancer diagnoses are encouraging because it achieves the most number of correct diagnoses when benchmarked against other computational intelligence based models. More importantly, its automatically derived rules are consistent with expert knowledge.
URI: https://hdl.handle.net/10356/89612
http://hdl.handle.net/10220/46272
ISSN: 1568-4946
DOI: http://dx.doi.org/10.1016/j.asoc.2013.12.018
Rights: © 2014 Elsevier. This is the author created version of a work that has been peer reviewed and accepted for publication by Applied Soft Computing, Elsevier. It incorporates referee’s comments but changes resulting from the publishing process, such as copyediting, structural formatting, may not be reflected in this document. The published version is available at: [http://dx.doi.org/10.1016/j.asoc.2013.12.018].
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Journal Articles

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