GA-based Feature Selection with ANFIS Approach to Breast Cancer Recurrence
Automatic disease diagnosis systems are important for medical fields. These systems have been used to help doctors to make better diagnosis. Breast cancer is a very common class of cancers among women. In this paper, we focus on breast cancer recurrence problem, hybridizing two methodologies, Genetic Algorithm (GA) and Adaptive Neuro Fuzzy Inference System (ANFIS), to develop a good diagnosis system. GA has been used as a selection algorithm to find the best features, whilst ANFIS has been used as a classifier algorithm. The robustness of the proposed hybrid methodology is examined using classification accuracy, sensitivity, and specificity. The proposed hybrid algorithm has achieved accuracy of 88% for training dataset and 71% for testing. The results demonstrate the effective interpretation and point out the ability to design a good diagnosis system
Keywords: Breast cancer; Feature Selection; Genetic Algorithm; ANFIS
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ABOUT THE AUTHOR
Hamza Turabieh
is an Assistant professor at Computer Science department- Faculty of Science and Information Technology- Taif University. Hamza Turabieh received his BA, M.Sc. degrees in Computer Science from Balqa Applied University in 2004 and 2006 respectively in Jordan. Turabieh obtained his PhD from National University of Malaysia (UKM) in 2010, his research interests and activities lie at the interface of Computer Science and Operational Research. Intelligent decision support systems, search and optimization (combinatorial optimization, constraint optimization, multi-modal optimization and multi-objective optimization) using heuristics, local search, hyper-heuristics, met heuristics (in particular memetic algorithms, particle swarm optimization), hybrid approaches and their theoretical foundations.
Hamza Turabieh
is an Assistant professor at Computer Science department- Faculty of Science and Information Technology- Taif University. Hamza Turabieh received his BA, M.Sc. degrees in Computer Science from Balqa Applied University in 2004 and 2006 respectively in Jordan. Turabieh obtained his PhD from National University of Malaysia (UKM) in 2010, his research interests and activities lie at the interface of Computer Science and Operational Research. Intelligent decision support systems, search and optimization (combinatorial optimization, constraint optimization, multi-modal optimization and multi-objective optimization) using heuristics, local search, hyper-heuristics, met heuristics (in particular memetic algorithms, particle swarm optimization), hybrid approaches and their theoretical foundations.