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Healthcare: Prediction of Breast Cancer Stage Using Social Spider-Inspired Optimization Algorithm

Healthcare: Prediction of Breast Cancer Stage Using Social Spider-Inspired Optimization Algorithm

Ramani Selvanambi, Jaisankar N.
Copyright: © 2019 |Volume: 10 |Issue: 2 |Pages: 23
ISSN: 1947-315X|EISSN: 1947-3168|EISBN13: 9781522566656|DOI: 10.4018/IJEHMC.2019040104
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MLA

Selvanambi, Ramani, and Jaisankar N. "Healthcare: Prediction of Breast Cancer Stage Using Social Spider-Inspired Optimization Algorithm." IJEHMC vol.10, no.2 2019: pp.63-85. http://doi.org/10.4018/IJEHMC.2019040104

APA

Selvanambi, R. & Jaisankar N. (2019). Healthcare: Prediction of Breast Cancer Stage Using Social Spider-Inspired Optimization Algorithm. International Journal of E-Health and Medical Communications (IJEHMC), 10(2), 63-85. http://doi.org/10.4018/IJEHMC.2019040104

Chicago

Selvanambi, Ramani, and Jaisankar N. "Healthcare: Prediction of Breast Cancer Stage Using Social Spider-Inspired Optimization Algorithm," International Journal of E-Health and Medical Communications (IJEHMC) 10, no.2: 63-85. http://doi.org/10.4018/IJEHMC.2019040104

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Abstract

Quality analysis of the treatment of cancer has been an objective of e-health services for quite some time. The objective is to predict the stage of breast cancer by using diverse input parameters. Breast cancer is one of the main causes of death in women when compared to other tumors. The classification of breast cancer information can be profitable to anticipate diseases or track the hereditary of tumors. For classification, an artificial neural network (ANN) structure was carried out. In the structure, nine training algorithms are used and the proposed is the Levenberg-Marquardt algorithm. For optimizing the hidden layer and neuron, three optimization techniques are used. In the result, the best approval execution is anticipated and the diverse execution evaluation estimation for three optimization algorithms is researched. The correlation execution diagram for an accuracy of 95%, a sensitivity of 98%, and a specificity of 89% of a social spider optimization (SSO) algorithm are shown.

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