Bio-inspired Approach for Early Diabetes Prediction and Diet Recommendation
Abstract Diabetes mellitus is one of the hyperglycemic diseases. To meet with an objective of early prediction of diabetes, the paper comprises of case studies of diabetes patients, the existing working models used to predict diabetes in patients. This study aimed to use the nature-inspired metaheur...
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| Autor*in: |
Jain, Anjali [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2024 |
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| Schlagwörter: |
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| Anmerkung: |
© The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd 2024. 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: SN Computer Science - Singapore : Springer Singapore, 2020, 5(2024), 1 vom: 10. Jan. |
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| Übergeordnetes Werk: |
volume:5 ; year:2024 ; number:1 ; day:10 ; month:01 |
| Links: |
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| DOI / URN: |
10.1007/s42979-023-02481-x |
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| Katalog-ID: |
SPR05434476X |
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| 520 | |a Abstract Diabetes mellitus is one of the hyperglycemic diseases. To meet with an objective of early prediction of diabetes, the paper comprises of case studies of diabetes patients, the existing working models used to predict diabetes in patients. This study aimed to use the nature-inspired metaheuristic algorithms like ant colony optimization, Bat Algorithm, Cuttlefish Algorithm, Elephant Herd Optimization Algorithm, and Artificial Bee Algorithm, etc. which are usually utilized for numerical analysis such as accuracy and other performance metrics. The objective was to develop a model that accurately recognizes diabetes by employing algorithms influenced by nature on a particular dataset. Diabetes was detected using several classification algorithms, and the accuracy of the classifiers was improved by tuning their hyperparameters using Hybrid Bat Algorithm. Most of the classifiers in use have their performance improved using various techniques. With the maximum accuracy of 98%, the voting classifier along with Smote and Bat Algorithm exceeded the competition, which is shown and discussed in the paper. The focus is on diabetes prediction, and then delve into the dietary recommendation aspect. | ||
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10.1007/s42979-023-02481-x doi (DE-627)SPR05434476X (SPR)s42979-023-02481-x-e DE-627 ger DE-627 rakwb eng Jain, Anjali verfasserin (orcid)0000-0003-2183-9393 aut Bio-inspired Approach for Early Diabetes Prediction and Diet Recommendation 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd 2024. 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 Diabetes mellitus is one of the hyperglycemic diseases. To meet with an objective of early prediction of diabetes, the paper comprises of case studies of diabetes patients, the existing working models used to predict diabetes in patients. This study aimed to use the nature-inspired metaheuristic algorithms like ant colony optimization, Bat Algorithm, Cuttlefish Algorithm, Elephant Herd Optimization Algorithm, and Artificial Bee Algorithm, etc. which are usually utilized for numerical analysis such as accuracy and other performance metrics. The objective was to develop a model that accurately recognizes diabetes by employing algorithms influenced by nature on a particular dataset. Diabetes was detected using several classification algorithms, and the accuracy of the classifiers was improved by tuning their hyperparameters using Hybrid Bat Algorithm. Most of the classifiers in use have their performance improved using various techniques. With the maximum accuracy of 98%, the voting classifier along with Smote and Bat Algorithm exceeded the competition, which is shown and discussed in the paper. The focus is on diabetes prediction, and then delve into the dietary recommendation aspect. Ada boost (dpeaa)DE-He213 BMI (dpeaa)DE-He213 BMR (dpeaa)DE-He213 Bat algorithm (dpeaa)DE-He213 Random forest (dpeaa)DE-He213 Smote (dpeaa)DE-He213 Support vector machine (dpeaa)DE-He213 Singhal, Alka aut Enthalten in SN Computer Science Singapore : Springer Singapore, 2020 5(2024), 1 vom: 10. Jan. (DE-627)1668832976 (DE-600)2977367-2 2661-8907 nnns volume:5 year:2024 number:1 day:10 month:01 https://dx.doi.org/10.1007/s42979-023-02481-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 5 2024 1 10 01 |
| spelling |
10.1007/s42979-023-02481-x doi (DE-627)SPR05434476X (SPR)s42979-023-02481-x-e DE-627 ger DE-627 rakwb eng Jain, Anjali verfasserin (orcid)0000-0003-2183-9393 aut Bio-inspired Approach for Early Diabetes Prediction and Diet Recommendation 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd 2024. 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 Diabetes mellitus is one of the hyperglycemic diseases. To meet with an objective of early prediction of diabetes, the paper comprises of case studies of diabetes patients, the existing working models used to predict diabetes in patients. This study aimed to use the nature-inspired metaheuristic algorithms like ant colony optimization, Bat Algorithm, Cuttlefish Algorithm, Elephant Herd Optimization Algorithm, and Artificial Bee Algorithm, etc. which are usually utilized for numerical analysis such as accuracy and other performance metrics. The objective was to develop a model that accurately recognizes diabetes by employing algorithms influenced by nature on a particular dataset. Diabetes was detected using several classification algorithms, and the accuracy of the classifiers was improved by tuning their hyperparameters using Hybrid Bat Algorithm. Most of the classifiers in use have their performance improved using various techniques. With the maximum accuracy of 98%, the voting classifier along with Smote and Bat Algorithm exceeded the competition, which is shown and discussed in the paper. The focus is on diabetes prediction, and then delve into the dietary recommendation aspect. Ada boost (dpeaa)DE-He213 BMI (dpeaa)DE-He213 BMR (dpeaa)DE-He213 Bat algorithm (dpeaa)DE-He213 Random forest (dpeaa)DE-He213 Smote (dpeaa)DE-He213 Support vector machine (dpeaa)DE-He213 Singhal, Alka aut Enthalten in SN Computer Science Singapore : Springer Singapore, 2020 5(2024), 1 vom: 10. Jan. (DE-627)1668832976 (DE-600)2977367-2 2661-8907 nnns volume:5 year:2024 number:1 day:10 month:01 https://dx.doi.org/10.1007/s42979-023-02481-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 5 2024 1 10 01 |
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10.1007/s42979-023-02481-x doi (DE-627)SPR05434476X (SPR)s42979-023-02481-x-e DE-627 ger DE-627 rakwb eng Jain, Anjali verfasserin (orcid)0000-0003-2183-9393 aut Bio-inspired Approach for Early Diabetes Prediction and Diet Recommendation 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd 2024. 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 Diabetes mellitus is one of the hyperglycemic diseases. To meet with an objective of early prediction of diabetes, the paper comprises of case studies of diabetes patients, the existing working models used to predict diabetes in patients. This study aimed to use the nature-inspired metaheuristic algorithms like ant colony optimization, Bat Algorithm, Cuttlefish Algorithm, Elephant Herd Optimization Algorithm, and Artificial Bee Algorithm, etc. which are usually utilized for numerical analysis such as accuracy and other performance metrics. The objective was to develop a model that accurately recognizes diabetes by employing algorithms influenced by nature on a particular dataset. Diabetes was detected using several classification algorithms, and the accuracy of the classifiers was improved by tuning their hyperparameters using Hybrid Bat Algorithm. Most of the classifiers in use have their performance improved using various techniques. With the maximum accuracy of 98%, the voting classifier along with Smote and Bat Algorithm exceeded the competition, which is shown and discussed in the paper. The focus is on diabetes prediction, and then delve into the dietary recommendation aspect. Ada boost (dpeaa)DE-He213 BMI (dpeaa)DE-He213 BMR (dpeaa)DE-He213 Bat algorithm (dpeaa)DE-He213 Random forest (dpeaa)DE-He213 Smote (dpeaa)DE-He213 Support vector machine (dpeaa)DE-He213 Singhal, Alka aut Enthalten in SN Computer Science Singapore : Springer Singapore, 2020 5(2024), 1 vom: 10. Jan. (DE-627)1668832976 (DE-600)2977367-2 2661-8907 nnns volume:5 year:2024 number:1 day:10 month:01 https://dx.doi.org/10.1007/s42979-023-02481-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 5 2024 1 10 01 |
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10.1007/s42979-023-02481-x doi (DE-627)SPR05434476X (SPR)s42979-023-02481-x-e DE-627 ger DE-627 rakwb eng Jain, Anjali verfasserin (orcid)0000-0003-2183-9393 aut Bio-inspired Approach for Early Diabetes Prediction and Diet Recommendation 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd 2024. 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 Diabetes mellitus is one of the hyperglycemic diseases. To meet with an objective of early prediction of diabetes, the paper comprises of case studies of diabetes patients, the existing working models used to predict diabetes in patients. This study aimed to use the nature-inspired metaheuristic algorithms like ant colony optimization, Bat Algorithm, Cuttlefish Algorithm, Elephant Herd Optimization Algorithm, and Artificial Bee Algorithm, etc. which are usually utilized for numerical analysis such as accuracy and other performance metrics. The objective was to develop a model that accurately recognizes diabetes by employing algorithms influenced by nature on a particular dataset. Diabetes was detected using several classification algorithms, and the accuracy of the classifiers was improved by tuning their hyperparameters using Hybrid Bat Algorithm. Most of the classifiers in use have their performance improved using various techniques. With the maximum accuracy of 98%, the voting classifier along with Smote and Bat Algorithm exceeded the competition, which is shown and discussed in the paper. The focus is on diabetes prediction, and then delve into the dietary recommendation aspect. Ada boost (dpeaa)DE-He213 BMI (dpeaa)DE-He213 BMR (dpeaa)DE-He213 Bat algorithm (dpeaa)DE-He213 Random forest (dpeaa)DE-He213 Smote (dpeaa)DE-He213 Support vector machine (dpeaa)DE-He213 Singhal, Alka aut Enthalten in SN Computer Science Singapore : Springer Singapore, 2020 5(2024), 1 vom: 10. Jan. (DE-627)1668832976 (DE-600)2977367-2 2661-8907 nnns volume:5 year:2024 number:1 day:10 month:01 https://dx.doi.org/10.1007/s42979-023-02481-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 5 2024 1 10 01 |
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Jain, Anjali misc Ada boost misc BMI misc BMR misc Bat algorithm misc Random forest misc Smote misc Support vector machine Bio-inspired Approach for Early Diabetes Prediction and Diet Recommendation |
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Bio-inspired Approach for Early Diabetes Prediction and Diet Recommendation Ada boost (dpeaa)DE-He213 BMI (dpeaa)DE-He213 BMR (dpeaa)DE-He213 Bat algorithm (dpeaa)DE-He213 Random forest (dpeaa)DE-He213 Smote (dpeaa)DE-He213 Support vector machine (dpeaa)DE-He213 |
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bio-inspired approach for early diabetes prediction and diet recommendation |
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Bio-inspired Approach for Early Diabetes Prediction and Diet Recommendation |
| abstract |
Abstract Diabetes mellitus is one of the hyperglycemic diseases. To meet with an objective of early prediction of diabetes, the paper comprises of case studies of diabetes patients, the existing working models used to predict diabetes in patients. This study aimed to use the nature-inspired metaheuristic algorithms like ant colony optimization, Bat Algorithm, Cuttlefish Algorithm, Elephant Herd Optimization Algorithm, and Artificial Bee Algorithm, etc. which are usually utilized for numerical analysis such as accuracy and other performance metrics. The objective was to develop a model that accurately recognizes diabetes by employing algorithms influenced by nature on a particular dataset. Diabetes was detected using several classification algorithms, and the accuracy of the classifiers was improved by tuning their hyperparameters using Hybrid Bat Algorithm. Most of the classifiers in use have their performance improved using various techniques. With the maximum accuracy of 98%, the voting classifier along with Smote and Bat Algorithm exceeded the competition, which is shown and discussed in the paper. The focus is on diabetes prediction, and then delve into the dietary recommendation aspect. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd 2024. 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. |
| abstractGer |
Abstract Diabetes mellitus is one of the hyperglycemic diseases. To meet with an objective of early prediction of diabetes, the paper comprises of case studies of diabetes patients, the existing working models used to predict diabetes in patients. This study aimed to use the nature-inspired metaheuristic algorithms like ant colony optimization, Bat Algorithm, Cuttlefish Algorithm, Elephant Herd Optimization Algorithm, and Artificial Bee Algorithm, etc. which are usually utilized for numerical analysis such as accuracy and other performance metrics. The objective was to develop a model that accurately recognizes diabetes by employing algorithms influenced by nature on a particular dataset. Diabetes was detected using several classification algorithms, and the accuracy of the classifiers was improved by tuning their hyperparameters using Hybrid Bat Algorithm. Most of the classifiers in use have their performance improved using various techniques. With the maximum accuracy of 98%, the voting classifier along with Smote and Bat Algorithm exceeded the competition, which is shown and discussed in the paper. The focus is on diabetes prediction, and then delve into the dietary recommendation aspect. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd 2024. 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 Diabetes mellitus is one of the hyperglycemic diseases. To meet with an objective of early prediction of diabetes, the paper comprises of case studies of diabetes patients, the existing working models used to predict diabetes in patients. This study aimed to use the nature-inspired metaheuristic algorithms like ant colony optimization, Bat Algorithm, Cuttlefish Algorithm, Elephant Herd Optimization Algorithm, and Artificial Bee Algorithm, etc. which are usually utilized for numerical analysis such as accuracy and other performance metrics. The objective was to develop a model that accurately recognizes diabetes by employing algorithms influenced by nature on a particular dataset. Diabetes was detected using several classification algorithms, and the accuracy of the classifiers was improved by tuning their hyperparameters using Hybrid Bat Algorithm. Most of the classifiers in use have their performance improved using various techniques. With the maximum accuracy of 98%, the voting classifier along with Smote and Bat Algorithm exceeded the competition, which is shown and discussed in the paper. The focus is on diabetes prediction, and then delve into the dietary recommendation aspect. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd 2024. 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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Bio-inspired Approach for Early Diabetes Prediction and Diet Recommendation |
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Singhal, Alka |
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2024-07-04T01:09:58.082Z |
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