A fuzzy-genetic approach to breast cancer diagnosis
The automatic diagnosis of breast cancer is an important, real-world medical problem. In this paper we focus on the Wisconsin breast cancer diagnosis (WBCD) problem, combining two methodologies—fuzzy systems and evolutionary algorithms—so as to automatically produce diagnostic systems. We find that...
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| Veröffentlicht in: | Artificial intelligence in medicine 1999-10, Vol.17 (2), p.131-155 |
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| creator | Peña-Reyes, Carlos Andrés Sipper, Moshe |
| description | The automatic diagnosis of breast cancer is an important, real-world medical problem. In this paper we focus on the Wisconsin breast cancer diagnosis (WBCD) problem, combining two methodologies—fuzzy systems and evolutionary algorithms—so as to automatically produce diagnostic systems. We find that our fuzzy-genetic approach produces systems exhibiting two prime characteristics: first, they attain high classification performance (the best shown to date), with the possibility of attributing a confidence measure to the output diagnosis; second, the resulting systems involve a few simple rules, and are therefore (human-) interpretable. |
| doi_str_mv | 10.1016/S0933-3657(99)00019-6 |
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| ispartof | Artificial intelligence in medicine, 1999-10, Vol.17 (2), p.131-155 |
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| language | eng |
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| source | MEDLINE; ScienceDirect Journals (5 years ago - present) |
| subjects | Algorithms Artificial Intelligence Breast cancer Breast cancer diagnosis Breast Neoplasms - diagnosis Breast Neoplasms - genetics Computer applications Databases, Factual Diagnosis, Computer-Assisted - methods Diagnostic systems Evolutionary algorithms Female Fuzzy Logic Fuzzy sets Fuzzy systems Genetic algorithms Genome Humans Medicine Models, Biological Oncology |
| title | A fuzzy-genetic approach to breast cancer diagnosis |
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