A Genetic-Fuzzy System Algorithm Method for the Breast Cancer Diagnosis Problem
DOI:
Abstract
Breast cancer is an important medical problem, especially for women, computer-aided medical diagnosis is very important in terms of prevention and early detection. This paper presents early detection of breast cancer using two methods, namely genetic algorithm and fuzzy inference system which will be used for early detection of breast cancer which will be used by doctors with computer assistance to obtain medical diagnosis of breast cancer in Indonesia. Our research shows that the diagnosis of breast cancer using these two methods has a high level of accuracy.
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References
M. N. Afifah, “10 Penyebab Kanker Payudara dan Cara Mencegahnya,” Kompas, 2021. [Online]. Available: https: //health.kompas.com/read/2021/10/06/200100168/10-penyebab-kankerpayu dara-dan-cara-mencegahnya. [Accessed: October 2021].
Anonymous, “Kanker,” WHO, 2019. [Online]. Available: https: //www.who.int/indonesia. [Accessed: March 2021].
J. Han, and M. Kamber, Data Mining Concept and Techniques. USA: Morgan Kaufmant, 2006.
N. Maleki, Y. Zeinali, and S. T. A. Niaki, “A k-NN Method for Lung Cancer Prognosis with the Use of A Genetic Algorithm for Feature Selection,” Expert Systems with Applications, vol. 164, 2021. doi: 10.1016/j.eswa.2020.113981.
D. F. Taino, M. G. Ribeiro, and G. F. Roberto, “Analysis of Cancer iIn Histological Images: Employing an Approach Based on Genetic Algorithm,” Pattern Analytic Application, vol. 24, pp. 483–496, 2021. doi: 10.1007/s10044-020-00931-3
P. Chandrakar, K. Shrivas, A. Kumar, and S. Neelam, “Design of a Novel Ensemble Model of Classification Technique for Gene-Expression Data of Lung Cancer with Modified Genetic Algorithm,” Endorsed Transactions on Pervasive Health and Technology, vol. 7, no. 25, 2021.
S. Jijitha and T. Amudha, “Breast Cancer Prognosis Using Machine Learning Techniques and Genetic Algorithm: Experiment on Six Different Datasets,” in Evolutionary Computing and Mobile Sustainable Networks. Singapore: Springer, 2019.
R. Roger, S. Lincoln, A. S. Adriel, M. Petrucio, M. Débora, and C. Aura, “Combining Genetic Algorithms and SVM for Breast Cancer Diagnosis Using Infrared Thermography,” Sensors, vol. 21, no. 14, pp. 1424-8220, 2021.
W. Wu, H. Huang, Z. Zhou, and Z. Liu, A Genetic Algorithm-Based Routing Service for Simulation Grid Multi-focus Image Fusion Based on SOFM Neural Networks and Evolution Strategies. Berlin Heidelberg: Springer, 2005.
E. M. Golafshani, A. Behnood, S. S. Hosseinikebria, and M. Arashpour, “Novel metaheuristic-Based Type-2 Fuzzy Inference System for Predicting the Compressive Strength of Recycled Aggregate Concrete,” Journal of Cleaner Production, vol. 320, 2021. doi: 10.1016/j.jclepro.2021.128771.
M. Parsajoo, A. S. Mohammed, S. Yagiz, D. J. Armaghani, and M. Khandelwal, “An evolutionary Adaptive Neuro-Fuzzy Inference System for Estimating Field Penetration Index of Tunnel Boring Machine in Rock Mass,” Journal of Rock Mechanics and Geotechnical Engineering, vol. 13, no. 6, 2021. doi: 10.1016/j.jrmge.2021.05.010.
M. E. Zayed, J. Zhao, W. Li, A. H. Elsheikh, and M. A. Elaziz, “A Hybrid Adaptive Neuro-Fuzzy Inference System Integrated with Equilibrium Optimizer Algorithm for Predicting the Energetic Performance of Solar Dish Collector,” Energy, vol. 235, 2021. doi: 10.1016/j.energy.2021.121289.
J. M. Keller, D. Liu, and D. B. Fogel, Fundamental of Computational Intellegence. Canada: Press Wiley, 2016.
J. Luis, G. Arroyo and B. G. Zapirain, Computer in Biology and Medicine: Algorithm for Segmentation. USA: Springer, 2019.
C. H. Tan, M. S. Tan, MS. W. Chang, K. S. Yap, H. J. Yap, and S. Y. Wong, “Genetic Algorithm Fuzzy Logic for Medical Knowledge-Based Pattern Classification,” Journal of Engineering Science and Technology, vol. 13, pp. 242-258, 2018.
T. M. Mitchell, Machine Learning. USA: McGraw Hill, 1997.
