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This paper aims to design a robust model for diagnosis of diabetes using a hybrid approach of Chaotic-Jaya (CJaya) algorithm with Extreme Learning Machine (ELM), which is named as CJaya-ELM. In this paper, Jaya algorithm with Chaotic learning approach is used to optimize the random parameters of ELM classifier. Here, to assess the efficacy of the designed model, Pima Indian diabetes dataset is considered. Here, the designed model CJaya-ELM, has been compared with basic ELM, Teaching Learning Based Optimization algorithm (TLBO) optimized ELM (TLBO-ELM), Multi-Layer Perceptron (MLP), Jaya algorithm optimized MLP (Jaya-MLP), TLBO algorithm optimized MLP (TLBO-MLP) and CJaya algorithm optimized MLP models. CJaya-ELM model resulted in the highest testing accuracy of 0.9687, sensitivity of 1, specificity of 0.9688 with 0.9782 area under curve (AUC) value. Results reveal that CJaya-ELM model effectively classifies both the positive and negative samples of Pima and outperforms the competitors.<\/jats:p>","DOI":"10.1515\/jib-2019-0097","type":"journal-article","created":{"date-parts":[[2020,8,18]],"date-time":"2020-08-18T07:53:43Z","timestamp":1597737223000},"page":"81-99","source":"Crossref","is-referenced-by-count":8,"title":["Diagnosis of diabetes in pregnant woman using a Chaotic-Jaya hybridized extreme learning machine model"],"prefix":"10.1515","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8750-7370","authenticated-orcid":false,"given":"Prajna Paramita","family":"Debata","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering , International Institute of Information Technology , Bhubaneswar , Odisha , India"}]},{"given":"Puspanjali","family":"Mohapatra","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering , International Institute of Information Technology , Bhubaneswar , Odisha , India"}]}],"member":"374","published-online":{"date-parts":[[2020,8,13]]},"reference":[{"key":"2023033120072473169_j_jib-2019-0097_ref_001_w2aab3b7d122b1b6b1ab2b1b1Aa","unstructured":"National Diabetes Information Clearinghouse (NDIC); 2011. 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