WoM-based deep BiLSTM: smart disease prediction model using WoM-based deep BiLSTM classifier
Abstract Diagnosis of cardiovascular disease has been significant due to the increased number of people affected by cardiovascular diseases. Though various methods were developed in classifying the diseases and ensuring the privacy for secure data transfer, most of the existing methods suffer from a...
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| Autor*in: |
Dhaka, Priyanka [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2023 |
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| Schlagwörter: |
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© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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| Übergeordnetes Werk: |
Enthalten in: Multimedia tools and applications - Springer US, 1995, 82(2023), 16 vom: 20. Jan., Seite 25061-25082 |
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| Übergeordnetes Werk: |
volume:82 ; year:2023 ; number:16 ; day:20 ; month:01 ; pages:25061-25082 |
| Links: |
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| DOI / URN: |
10.1007/s11042-023-14336-x |
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OLC2144036799 |
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| 520 | |a Abstract Diagnosis of cardiovascular disease has been significant due to the increased number of people affected by cardiovascular diseases. Though various methods were developed in classifying the diseases and ensuring the privacy for secure data transfer, most of the existing methods suffer from accurate decision making. Hence, this research tends to introduce a well-suited disease prediction model with the help of an improved deep Bidirectional Long Short Term Memory (Deep BiLSTM). The hyper-parameters related to the optimized deep BiLSTM classifier are tuned by the proposed optimization named Whale-on-Marine optimization (WoM) algorithm. The research-based on the developed deep BiLSTM classifier for heart disease utilizes the Elliptic Curve Cryptography (ECC) dependent Diffi-Huffman algorithm to ensure secure data transmission in the network. The effective decision making and secure data transfer are performed using the WoM-based deep BiLSTM classifier, where the WoM optimization reflects the characteristics of marine and Whale to determine the space between the prey and the predators, which improves the exploitation integration and exploitation tendencies. The performance metrics reveal that the proposed optimization based on deep BiLSTM effectively predicts heart diseases. The optimized deep BiLSTM classifier achieves a sensitivity of 97.93%, specificity of 97.52%, F-Measure of 97.658% and accuracy of 97.53% for the training percentage in Statlog, Cleveland, and Hungary database. | ||
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10.1007/s11042-023-14336-x doi (DE-627)OLC2144036799 (DE-He213)s11042-023-14336-x-p DE-627 ger DE-627 rakwb eng 070 004 VZ Dhaka, Priyanka verfasserin aut WoM-based deep BiLSTM: smart disease prediction model using WoM-based deep BiLSTM classifier 2023 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Diagnosis of cardiovascular disease has been significant due to the increased number of people affected by cardiovascular diseases. Though various methods were developed in classifying the diseases and ensuring the privacy for secure data transfer, most of the existing methods suffer from accurate decision making. Hence, this research tends to introduce a well-suited disease prediction model with the help of an improved deep Bidirectional Long Short Term Memory (Deep BiLSTM). The hyper-parameters related to the optimized deep BiLSTM classifier are tuned by the proposed optimization named Whale-on-Marine optimization (WoM) algorithm. The research-based on the developed deep BiLSTM classifier for heart disease utilizes the Elliptic Curve Cryptography (ECC) dependent Diffi-Huffman algorithm to ensure secure data transmission in the network. The effective decision making and secure data transfer are performed using the WoM-based deep BiLSTM classifier, where the WoM optimization reflects the characteristics of marine and Whale to determine the space between the prey and the predators, which improves the exploitation integration and exploitation tendencies. The performance metrics reveal that the proposed optimization based on deep BiLSTM effectively predicts heart diseases. The optimized deep BiLSTM classifier achieves a sensitivity of 97.93%, specificity of 97.52%, F-Measure of 97.658% and accuracy of 97.53% for the training percentage in Statlog, Cleveland, and Hungary database. Deep BiLSTM WoM optimization Heart disease prediction ECC based Diffi-Huffman algorithm Nagpal, Bharti aut Enthalten in Multimedia tools and applications Springer US, 1995 82(2023), 16 vom: 20. Jan., Seite 25061-25082 (DE-627)189064145 (DE-600)1287642-2 (DE-576)052842126 1380-7501 nnns volume:82 year:2023 number:16 day:20 month:01 pages:25061-25082 https://doi.org/10.1007/s11042-023-14336-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-BUB SSG-OLC-MKW AR 82 2023 16 20 01 25061-25082 |
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10.1007/s11042-023-14336-x doi (DE-627)OLC2144036799 (DE-He213)s11042-023-14336-x-p DE-627 ger DE-627 rakwb eng 070 004 VZ Dhaka, Priyanka verfasserin aut WoM-based deep BiLSTM: smart disease prediction model using WoM-based deep BiLSTM classifier 2023 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Diagnosis of cardiovascular disease has been significant due to the increased number of people affected by cardiovascular diseases. Though various methods were developed in classifying the diseases and ensuring the privacy for secure data transfer, most of the existing methods suffer from accurate decision making. Hence, this research tends to introduce a well-suited disease prediction model with the help of an improved deep Bidirectional Long Short Term Memory (Deep BiLSTM). The hyper-parameters related to the optimized deep BiLSTM classifier are tuned by the proposed optimization named Whale-on-Marine optimization (WoM) algorithm. The research-based on the developed deep BiLSTM classifier for heart disease utilizes the Elliptic Curve Cryptography (ECC) dependent Diffi-Huffman algorithm to ensure secure data transmission in the network. The effective decision making and secure data transfer are performed using the WoM-based deep BiLSTM classifier, where the WoM optimization reflects the characteristics of marine and Whale to determine the space between the prey and the predators, which improves the exploitation integration and exploitation tendencies. The performance metrics reveal that the proposed optimization based on deep BiLSTM effectively predicts heart diseases. The optimized deep BiLSTM classifier achieves a sensitivity of 97.93%, specificity of 97.52%, F-Measure of 97.658% and accuracy of 97.53% for the training percentage in Statlog, Cleveland, and Hungary database. Deep BiLSTM WoM optimization Heart disease prediction ECC based Diffi-Huffman algorithm Nagpal, Bharti aut Enthalten in Multimedia tools and applications Springer US, 1995 82(2023), 16 vom: 20. Jan., Seite 25061-25082 (DE-627)189064145 (DE-600)1287642-2 (DE-576)052842126 1380-7501 nnns volume:82 year:2023 number:16 day:20 month:01 pages:25061-25082 https://doi.org/10.1007/s11042-023-14336-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-BUB SSG-OLC-MKW AR 82 2023 16 20 01 25061-25082 |
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10.1007/s11042-023-14336-x doi (DE-627)OLC2144036799 (DE-He213)s11042-023-14336-x-p DE-627 ger DE-627 rakwb eng 070 004 VZ Dhaka, Priyanka verfasserin aut WoM-based deep BiLSTM: smart disease prediction model using WoM-based deep BiLSTM classifier 2023 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Diagnosis of cardiovascular disease has been significant due to the increased number of people affected by cardiovascular diseases. Though various methods were developed in classifying the diseases and ensuring the privacy for secure data transfer, most of the existing methods suffer from accurate decision making. Hence, this research tends to introduce a well-suited disease prediction model with the help of an improved deep Bidirectional Long Short Term Memory (Deep BiLSTM). The hyper-parameters related to the optimized deep BiLSTM classifier are tuned by the proposed optimization named Whale-on-Marine optimization (WoM) algorithm. The research-based on the developed deep BiLSTM classifier for heart disease utilizes the Elliptic Curve Cryptography (ECC) dependent Diffi-Huffman algorithm to ensure secure data transmission in the network. The effective decision making and secure data transfer are performed using the WoM-based deep BiLSTM classifier, where the WoM optimization reflects the characteristics of marine and Whale to determine the space between the prey and the predators, which improves the exploitation integration and exploitation tendencies. The performance metrics reveal that the proposed optimization based on deep BiLSTM effectively predicts heart diseases. The optimized deep BiLSTM classifier achieves a sensitivity of 97.93%, specificity of 97.52%, F-Measure of 97.658% and accuracy of 97.53% for the training percentage in Statlog, Cleveland, and Hungary database. Deep BiLSTM WoM optimization Heart disease prediction ECC based Diffi-Huffman algorithm Nagpal, Bharti aut Enthalten in Multimedia tools and applications Springer US, 1995 82(2023), 16 vom: 20. Jan., Seite 25061-25082 (DE-627)189064145 (DE-600)1287642-2 (DE-576)052842126 1380-7501 nnns volume:82 year:2023 number:16 day:20 month:01 pages:25061-25082 https://doi.org/10.1007/s11042-023-14336-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-BUB SSG-OLC-MKW AR 82 2023 16 20 01 25061-25082 |
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10.1007/s11042-023-14336-x doi (DE-627)OLC2144036799 (DE-He213)s11042-023-14336-x-p DE-627 ger DE-627 rakwb eng 070 004 VZ Dhaka, Priyanka verfasserin aut WoM-based deep BiLSTM: smart disease prediction model using WoM-based deep BiLSTM classifier 2023 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Diagnosis of cardiovascular disease has been significant due to the increased number of people affected by cardiovascular diseases. Though various methods were developed in classifying the diseases and ensuring the privacy for secure data transfer, most of the existing methods suffer from accurate decision making. Hence, this research tends to introduce a well-suited disease prediction model with the help of an improved deep Bidirectional Long Short Term Memory (Deep BiLSTM). The hyper-parameters related to the optimized deep BiLSTM classifier are tuned by the proposed optimization named Whale-on-Marine optimization (WoM) algorithm. The research-based on the developed deep BiLSTM classifier for heart disease utilizes the Elliptic Curve Cryptography (ECC) dependent Diffi-Huffman algorithm to ensure secure data transmission in the network. The effective decision making and secure data transfer are performed using the WoM-based deep BiLSTM classifier, where the WoM optimization reflects the characteristics of marine and Whale to determine the space between the prey and the predators, which improves the exploitation integration and exploitation tendencies. The performance metrics reveal that the proposed optimization based on deep BiLSTM effectively predicts heart diseases. The optimized deep BiLSTM classifier achieves a sensitivity of 97.93%, specificity of 97.52%, F-Measure of 97.658% and accuracy of 97.53% for the training percentage in Statlog, Cleveland, and Hungary database. Deep BiLSTM WoM optimization Heart disease prediction ECC based Diffi-Huffman algorithm Nagpal, Bharti aut Enthalten in Multimedia tools and applications Springer US, 1995 82(2023), 16 vom: 20. Jan., Seite 25061-25082 (DE-627)189064145 (DE-600)1287642-2 (DE-576)052842126 1380-7501 nnns volume:82 year:2023 number:16 day:20 month:01 pages:25061-25082 https://doi.org/10.1007/s11042-023-14336-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-BUB SSG-OLC-MKW AR 82 2023 16 20 01 25061-25082 |
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10.1007/s11042-023-14336-x doi (DE-627)OLC2144036799 (DE-He213)s11042-023-14336-x-p DE-627 ger DE-627 rakwb eng 070 004 VZ Dhaka, Priyanka verfasserin aut WoM-based deep BiLSTM: smart disease prediction model using WoM-based deep BiLSTM classifier 2023 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Diagnosis of cardiovascular disease has been significant due to the increased number of people affected by cardiovascular diseases. Though various methods were developed in classifying the diseases and ensuring the privacy for secure data transfer, most of the existing methods suffer from accurate decision making. Hence, this research tends to introduce a well-suited disease prediction model with the help of an improved deep Bidirectional Long Short Term Memory (Deep BiLSTM). The hyper-parameters related to the optimized deep BiLSTM classifier are tuned by the proposed optimization named Whale-on-Marine optimization (WoM) algorithm. The research-based on the developed deep BiLSTM classifier for heart disease utilizes the Elliptic Curve Cryptography (ECC) dependent Diffi-Huffman algorithm to ensure secure data transmission in the network. The effective decision making and secure data transfer are performed using the WoM-based deep BiLSTM classifier, where the WoM optimization reflects the characteristics of marine and Whale to determine the space between the prey and the predators, which improves the exploitation integration and exploitation tendencies. The performance metrics reveal that the proposed optimization based on deep BiLSTM effectively predicts heart diseases. The optimized deep BiLSTM classifier achieves a sensitivity of 97.93%, specificity of 97.52%, F-Measure of 97.658% and accuracy of 97.53% for the training percentage in Statlog, Cleveland, and Hungary database. Deep BiLSTM WoM optimization Heart disease prediction ECC based Diffi-Huffman algorithm Nagpal, Bharti aut Enthalten in Multimedia tools and applications Springer US, 1995 82(2023), 16 vom: 20. Jan., Seite 25061-25082 (DE-627)189064145 (DE-600)1287642-2 (DE-576)052842126 1380-7501 nnns volume:82 year:2023 number:16 day:20 month:01 pages:25061-25082 https://doi.org/10.1007/s11042-023-14336-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-BUB SSG-OLC-MKW AR 82 2023 16 20 01 25061-25082 |
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WoM-based deep BiLSTM: smart disease prediction model using WoM-based deep BiLSTM classifier |
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WoM-based deep BiLSTM: smart disease prediction model using WoM-based deep BiLSTM classifier |
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Dhaka, Priyanka |
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Dhaka, Priyanka Nagpal, Bharti |
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wom-based deep bilstm: smart disease prediction model using wom-based deep bilstm classifier |
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WoM-based deep BiLSTM: smart disease prediction model using WoM-based deep BiLSTM classifier |
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Abstract Diagnosis of cardiovascular disease has been significant due to the increased number of people affected by cardiovascular diseases. Though various methods were developed in classifying the diseases and ensuring the privacy for secure data transfer, most of the existing methods suffer from accurate decision making. Hence, this research tends to introduce a well-suited disease prediction model with the help of an improved deep Bidirectional Long Short Term Memory (Deep BiLSTM). The hyper-parameters related to the optimized deep BiLSTM classifier are tuned by the proposed optimization named Whale-on-Marine optimization (WoM) algorithm. The research-based on the developed deep BiLSTM classifier for heart disease utilizes the Elliptic Curve Cryptography (ECC) dependent Diffi-Huffman algorithm to ensure secure data transmission in the network. The effective decision making and secure data transfer are performed using the WoM-based deep BiLSTM classifier, where the WoM optimization reflects the characteristics of marine and Whale to determine the space between the prey and the predators, which improves the exploitation integration and exploitation tendencies. The performance metrics reveal that the proposed optimization based on deep BiLSTM effectively predicts heart diseases. The optimized deep BiLSTM classifier achieves a sensitivity of 97.93%, specificity of 97.52%, F-Measure of 97.658% and accuracy of 97.53% for the training percentage in Statlog, Cleveland, and Hungary database. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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Abstract Diagnosis of cardiovascular disease has been significant due to the increased number of people affected by cardiovascular diseases. Though various methods were developed in classifying the diseases and ensuring the privacy for secure data transfer, most of the existing methods suffer from accurate decision making. Hence, this research tends to introduce a well-suited disease prediction model with the help of an improved deep Bidirectional Long Short Term Memory (Deep BiLSTM). The hyper-parameters related to the optimized deep BiLSTM classifier are tuned by the proposed optimization named Whale-on-Marine optimization (WoM) algorithm. The research-based on the developed deep BiLSTM classifier for heart disease utilizes the Elliptic Curve Cryptography (ECC) dependent Diffi-Huffman algorithm to ensure secure data transmission in the network. The effective decision making and secure data transfer are performed using the WoM-based deep BiLSTM classifier, where the WoM optimization reflects the characteristics of marine and Whale to determine the space between the prey and the predators, which improves the exploitation integration and exploitation tendencies. The performance metrics reveal that the proposed optimization based on deep BiLSTM effectively predicts heart diseases. The optimized deep BiLSTM classifier achieves a sensitivity of 97.93%, specificity of 97.52%, F-Measure of 97.658% and accuracy of 97.53% for the training percentage in Statlog, Cleveland, and Hungary database. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
| abstract_unstemmed |
Abstract Diagnosis of cardiovascular disease has been significant due to the increased number of people affected by cardiovascular diseases. Though various methods were developed in classifying the diseases and ensuring the privacy for secure data transfer, most of the existing methods suffer from accurate decision making. Hence, this research tends to introduce a well-suited disease prediction model with the help of an improved deep Bidirectional Long Short Term Memory (Deep BiLSTM). The hyper-parameters related to the optimized deep BiLSTM classifier are tuned by the proposed optimization named Whale-on-Marine optimization (WoM) algorithm. The research-based on the developed deep BiLSTM classifier for heart disease utilizes the Elliptic Curve Cryptography (ECC) dependent Diffi-Huffman algorithm to ensure secure data transmission in the network. The effective decision making and secure data transfer are performed using the WoM-based deep BiLSTM classifier, where the WoM optimization reflects the characteristics of marine and Whale to determine the space between the prey and the predators, which improves the exploitation integration and exploitation tendencies. The performance metrics reveal that the proposed optimization based on deep BiLSTM effectively predicts heart diseases. The optimized deep BiLSTM classifier achieves a sensitivity of 97.93%, specificity of 97.52%, F-Measure of 97.658% and accuracy of 97.53% for the training percentage in Statlog, Cleveland, and Hungary database. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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WoM-based deep BiLSTM: smart disease prediction model using WoM-based deep BiLSTM classifier |
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