Application of Ant Colony Algorithm and Principal Components Analysis in the Diagnosis of Lung Cancer
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Authors
Saeed Ayat
- Associate Professor, Department of Computer Engineering and Information Technology, Payame Noor University, Iran.
Mohsen Rahi
- M.Sc. student, Department of Computer Engineering and Information Technology, Payame Noor University, Iran.
Abstract
This paper presents a new method for diagnosing lung cancer by combination of ant colony algorithm, fuzzy logic and principal component analysis (PCA). In this method, PCA method is used to reduce the size of data sets, the fuzzy logic is used to create fuzzy rules that make it possible to be interpreted by experts. Finally, these fuzzy rules are optimized by ant colony algorithm (ACO). Evaluation and comparing the proposed method with other methods have been proposed to implement this approach, leading to lung cancer dataset with criteria such as speed, reliability, and the ability to interpret the show.
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ISRP Style
Saeed Ayat, Mohsen Rahi, Application of Ant Colony Algorithm and Principal Components Analysis in the Diagnosis of Lung Cancer, Journal of Mathematics and Computer Science, 13 (2014), no. 4, 343 - 352
AMA Style
Ayat Saeed, Rahi Mohsen, Application of Ant Colony Algorithm and Principal Components Analysis in the Diagnosis of Lung Cancer. J Math Comput SCI-JM. (2014); 13(4):343 - 352
Chicago/Turabian Style
Ayat, Saeed, Rahi, Mohsen. "Application of Ant Colony Algorithm and Principal Components Analysis in the Diagnosis of Lung Cancer." Journal of Mathematics and Computer Science, 13, no. 4 (2014): 343 - 352
Keywords
- lung cancer
- data mining
- ACO
- fuzzy logic
- PCA.
MSC
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