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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.<\/jats:p>","DOI":"10.4018\/ijehmc.2019040104","type":"journal-article","created":{"date-parts":[[2019,2,27]],"date-time":"2019-02-27T11:44:07Z","timestamp":1551267847000},"page":"63-85","source":"Crossref","is-referenced-by-count":3,"title":["Healthcare"],"prefix":"10.4018","volume":"10","author":[{"given":"Ramani","family":"Selvanambi","sequence":"first","affiliation":[{"name":"VIT University, Vellore, India"}]},{"family":"Jaisankar N.","sequence":"additional","affiliation":[{"name":"VIT University, Vellore, India"}]}],"member":"2432","reference":[{"key":"IJEHMC.2019040104-0","unstructured":"Kumar, G. R., Ramachandra, D. G., & Nagamani, K. (2013). An efficient prediction of breast cancer data using data mining techniques. 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