Breast Cancer Diagnosis Model Based on BP-GamysBoost

Abstract

Abstract: In view of the problem of unbalance of the breast cancer data, the standard Adaboost algorithm is improved. First, BP neural network is introduced, then the strong global optimization ability and fast convergence speed of simulated annealing genetic algorithm (SA-GA) are fused, and finally the weight is allocated reasonably to propose the BP-GamysBoost algorithm. At the same time, in order to verify the rationality of the proposed new algorithm BP-GamysBoost, the WBCD database is obtained from the UCI machine learning knowledge base, and the performance indexes such as stability, accuracy, missed diagnosis rate and sensitivity of BP-GamysBoost algorithm model are compared with BP model, BP-GA model and BP-Adaboost model. The results show that the BP-GamysBoost model works well in the breast cancer database and is superior to the other three algorithm models.

Key words: Adaboost algorithm, simulated annealing genetic algorithm, BP neural network, BP-GamysBoost model

LIU Jun, PENG Hui-xian, HUANG Bin, Tony SHAY. Breast Cancer Diagnosis Model Based on BP-GamysBoost[J]. Computer and Modernization, 2021, 0(04): 8-14.

References

［1］ BRAY F, FERLAY J, SOERJOMATARAM I, et al. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries［J］. CA: A Cancer Journal for Clinicians, 2018,68(6):394-424.
［2］ SIZILIO G R M A, LEITE C R M, GUERREIRO A M G, et al. Fuzzy method for pre-diagnosis of breast cancer from the fine needle aspirate analysis［J］. BioMedical Engineering Online, 2012,11. DOI: 10.1186/1475-925X-11-83.
［3］ MOROTA G, VENTURA R V, SILVA F F, et al. Machine learning and data mining advance predictive big data analysis in precision animal agriculture［J］. Journal of Animal Science, 2018,96(4):1540-1550.
［4］ KHUAT T T, GABRYS B. Accelerated learning algorithms of general fuzzy min-max neural network using a branch-and-bound-based hyperbox selection rule［J］. arXiv preprint arXiv:2003.11333, 2020.
［5］ KUMAR S A, KUMAR A, BAJAJ V, et al. An improved fuzzy min-max neural network for data classification［J］. IEEE Transactions on Fuzzy Systems, 2020,28(9):1910-1924.
［6］ BELLAZZI R, ZUPAN B. Predictive data mining in clinical medicine: Current issues and guidelines［J］. International Journal of Medical Informatics, 2008,77(2):81-97.
［7］ SINGH S, GUPTA P R, SHARMA M K. Breast cancer detection and classification of histopathological images［J］. International Journal of Engineering Science and Technology, 2011,3(5):4228-4232.
［8］ GEORGE Y M, ZAYED H H, ROUSHDY M I, et al. Remote computer-aided breast cancer detection and diagnosis system based on cytological images［J］. IEEE Systems Journal, 2014,8(3):949-964.
［9］ JELEN L, FEVENS T, KRZYZAK A. Classification of breast cancer malignancy using cytological images of fine needle aspiration biopsies［J］. International Journal of Applied Mathematics and Computer Science, 2008,18(1):75-83.
［10］NAHATO K B, HARICHANDRAN K N, ARPUTHARAJ K. Knowledge mining from clinical datasets using rough sets and backpropagation neural network［J］. Computational and Mathematical Methods in Medicine, 2015. DOI: 10.1155/2015/460189.
［11］ABDEL-ZAHER A M, ELDEIB A M. Breast cancer classification using deep belief networks［J］. Expert Systems with Applications, 2016,46:139-144.
［12］KAYMAK S, HELWAN A, UZUN D. Breast cancer image classification using artificial neural networks［J］. Procedia Computer Science, 2017,120:126-131.
［13］XIONG K. Research on the improvement of BP neural network algorithm and its application［J］. Advanced Materials Research, 2014,926-930:3216-3219.
［14］LIU X D, WANG Z W, ZHANG S T, et al. A novel approach to fuzzy cognitive map based on hesitant fuzzy sets for modeling risk impact on electric power system［J］. International Journal of Computational Intelligence Systems, 2019,12(2):842-854.
［15］BHARDWAJ A, TIWARI A. Breast cancer diagnosis using genetically optimized neural network model［J］. Expert Systems with Applications, 2015,42(10):4611-4620.
［16］ZHANG R, WU C. A simulated annealing algorithm based on block properties for the job shop scheduling problem with total weighted tardinessobjective［J］. Computers & Operations Research, 2011,38(5):854-867.
［17］ZHENG J, LIN D N, GAO Z J, et al. Deep learning assisted efficient Adaboost algorithm for breast cancer detection and early diagnosis［J］. IEEE Access, 2020,8:96946-96954.
［18］游士兵,严研. 逐步回归分析法及其应用［J］. 统计与决策, 2017(14):31-35.
［19］KERH T, SU Y H, MOSALLAM A. Incorporating global search capability of a genetic algorithm into neural computing to model seismic records and soil test data［J］. Neural Computing and Applications, 2017,28(3):437-448.
［20］ZHANG J H, ZHOU Z G. An improved genetic algorithm and its applications to the optimisation design of an aspirated compressor profile［J］. International Journal for Numerical Methods in Fluids, 2015,79(12):640-653.
［21］DONG H B, LI T, DING R, et al. A novel hybrid genetic algorithm with granular information for feature selection and optimization［J］. Applied Soft Computing, 2018,65:33-46.
［22］裴以建,杨亮亮,杨超杰. 基于一种混合遗传算法的移动机器人路径规划［J］. 现代电子技术, 2019,42(2):183-186.

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