Breast cancer is one of the major causes of death in women when compared to all other cancers. This cancer has become the most hazardous types of cancer among women in the world. Early detection of breast cancer is essential in reducing life losses. In this paper some metaheuristic optimization algorithms was used to find the parameters of the Fuzzy-ART. Fuzzy-ART is not so strong to deal with above data. However, its performance is significantly improved by using evolutionary optimization methods. These hybrid classification techniques were tested on a training data set provided by the Wisconsin dataset for breast cancer. Results showed that the proposed harmony search (HS) algorithm provides better result with less time and less number of steps than genetic algorithm (GA) and particle swarm optimization (PSO) in the same conditions. As seen in this research, evolutionary HS algorithm had a higher convergence ability to obtain optimal solution too. The best performance obtained from this algorithm is 97.80% for accuracy and 98.92% for specificity.
- Breast cancer diagnosis
- Metaheuristic optimization
- Genetic Algorithm
- Particle Swarm Optimization
- Harmony search algorithm
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Hassani, K., Jafarian, K. (2016). An Intelligent Method for Breast Cancer Diagnosis Based on Fuzzy ART and Metaheuristic Optimization. In: Kyriacou, E., Christofides, S., Pattichis, C. (eds) XIV Mediterranean Conference on Medical and Biological Engineering and Computing 2016. IFMBE Proceedings, vol 57. Springer, Cham. https://doi.org/10.1007/978-3-319-32703-7_41
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