Diagnosis of diabetes in pregnant woman using a Chaotic-Jaya hybridized extreme learning machine model
As stated by World Health Organization (WHO) report, 246 million individuals have suffered with diabetes disease over worldwide and it is anticipated that by 2025 this estimation can cross 380 million. So, the proper and quick diagnosis of this disease is turned into a significant challenge for the...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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De Gruyter
2020-08-01
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| Series: | Journal of Integrative Bioinformatics |
| Subjects: | |
| Online Access: | https://doi.org/10.1515/jib-2019-0097 |
| _version_ | 1818733598477385728 |
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| author | Debata Prajna Paramita Mohapatra Puspanjali |
| author_facet | Debata Prajna Paramita Mohapatra Puspanjali |
| author_sort | Debata Prajna Paramita |
| collection | DOAJ |
| description | As stated by World Health Organization (WHO) report, 246 million individuals have suffered with diabetes disease over worldwide and it is anticipated that by 2025 this estimation can cross 380 million. So, the proper and quick diagnosis of this disease is turned into a significant challenge for the machine learning researchers. 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. |
| first_indexed | 2024-12-17T23:52:01Z |
| format | Article |
| id | doaj.art-69cf6da6a98b46f786c6e793ed618eb0 |
| institution | Directory Open Access Journal |
| issn | 1613-4516 |
| language | English |
| last_indexed | 2024-12-17T23:52:01Z |
| publishDate | 2020-08-01 |
| publisher | De Gruyter |
| record_format | Article |
| series | Journal of Integrative Bioinformatics |
| spelling | doaj.art-69cf6da6a98b46f786c6e793ed618eb02022-12-21T21:28:11ZengDe GruyterJournal of Integrative Bioinformatics1613-45162020-08-01181819910.1515/jib-2019-0097Diagnosis of diabetes in pregnant woman using a Chaotic-Jaya hybridized extreme learning machine modelDebata Prajna Paramita0Mohapatra Puspanjali1Department of Computer Science and Engineering, International Institute of Information Technology, Bhubaneswar, Odisha, IndiaDepartment of Computer Science and Engineering, International Institute of Information Technology, Bhubaneswar, Odisha, IndiaAs stated by World Health Organization (WHO) report, 246 million individuals have suffered with diabetes disease over worldwide and it is anticipated that by 2025 this estimation can cross 380 million. So, the proper and quick diagnosis of this disease is turned into a significant challenge for the machine learning researchers. 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.https://doi.org/10.1515/jib-2019-0097chaotic jaya algorithmdiabetes diagnosisextreme learning machinemulti-layer perceptronoptimizationteaching learning based optimization |
| spellingShingle | Debata Prajna Paramita Mohapatra Puspanjali Diagnosis of diabetes in pregnant woman using a Chaotic-Jaya hybridized extreme learning machine model Journal of Integrative Bioinformatics chaotic jaya algorithm diabetes diagnosis extreme learning machine multi-layer perceptron optimization teaching learning based optimization |
| title | Diagnosis of diabetes in pregnant woman using a Chaotic-Jaya hybridized extreme learning machine model |
| title_full | Diagnosis of diabetes in pregnant woman using a Chaotic-Jaya hybridized extreme learning machine model |
| title_fullStr | Diagnosis of diabetes in pregnant woman using a Chaotic-Jaya hybridized extreme learning machine model |
| title_full_unstemmed | Diagnosis of diabetes in pregnant woman using a Chaotic-Jaya hybridized extreme learning machine model |
| title_short | Diagnosis of diabetes in pregnant woman using a Chaotic-Jaya hybridized extreme learning machine model |
| title_sort | diagnosis of diabetes in pregnant woman using a chaotic jaya hybridized extreme learning machine model |
| topic | chaotic jaya algorithm diabetes diagnosis extreme learning machine multi-layer perceptron optimization teaching learning based optimization |
| url | https://doi.org/10.1515/jib-2019-0097 |
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