Hypertension risk prediction models for patients with diabetes based on machine learning approaches
Abstract To construct effective prediction models for hypertension in diabetic patients based on machine learning. This study used electronic data from 2080 diabetic patients attending the specialized outpatient clinic for metabolic diseases of the Affiliated Hospital of Qingdao University from Marc...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Zhao, Yuxue [verfasserIn] Han, Jiashu [verfasserIn] Hu, Xinlin [verfasserIn] Hu, Bo [verfasserIn] Zhu, Hui [verfasserIn] Wang, Yanlong [verfasserIn] Zhu, Xiuli [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2023 |
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| Schlagwörter: |
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| Anmerkung: |
© 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, 83(2023), 20 vom: 28. Dez., Seite 59085-59102 |
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| Übergeordnetes Werk: |
volume:83 ; year:2023 ; number:20 ; day:28 ; month:12 ; pages:59085-59102 |
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| DOI / URN: |
10.1007/s11042-023-17926-x |
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SPR05609809X |
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| 520 | |a Abstract To construct effective prediction models for hypertension in diabetic patients based on machine learning. This study used electronic data from 2080 diabetic patients attending the specialized outpatient clinic for metabolic diseases of the Affiliated Hospital of Qingdao University from March 2017 to July 2020. We adopted 5 machine learning algorithms (artificial neural network, decision tree, random forest, support vector machine, Bayesian network) and constructed hypertension risk prediction models based on patients’ non-invasive variables. The study showed that artificial neural network (ANN) performed best, accuracy, sensitivity, specificity, and area under the receiver curve in the test set were 92.47%, 92.98%, 92.02%, 0.951, respectively. The prediction model showed that the top three predictors and their weight values were systolic blood pressure (w=0.3346), age (w=0.1437) and diastolic blood pressure (w=0.1236). Factors of daily living such as education level, activity, heart rate and fish intake also showed importance. ANN can apply non-invasive data to well predict the risk of secondary hypertension in diabetic patients. Healthcare providers could use this model to rapidly screen high-risk patients and instruct them to monitor blood pressure regularly and maintain a healthy lifestyle, thereby reducing the risk of hypertension in diabetic patients. The model is more suitable for areas with high morbidity of hypertension and poor socioeconomic conditions. It has important implications for achieving health management in a larger target population, in line with the international mission to improve the global burden of hypertension. | ||
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10.1007/s11042-023-17926-x doi (DE-627)SPR05609809X (SPR)s11042-023-17926-x-e DE-627 ger DE-627 rakwb eng 070 004 VZ 54.87 bkl Zhao, Yuxue verfasserin (orcid)0000-0003-1280-5298 aut Hypertension risk prediction models for patients with diabetes based on machine learning approaches 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr 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 To construct effective prediction models for hypertension in diabetic patients based on machine learning. This study used electronic data from 2080 diabetic patients attending the specialized outpatient clinic for metabolic diseases of the Affiliated Hospital of Qingdao University from March 2017 to July 2020. We adopted 5 machine learning algorithms (artificial neural network, decision tree, random forest, support vector machine, Bayesian network) and constructed hypertension risk prediction models based on patients’ non-invasive variables. The study showed that artificial neural network (ANN) performed best, accuracy, sensitivity, specificity, and area under the receiver curve in the test set were 92.47%, 92.98%, 92.02%, 0.951, respectively. The prediction model showed that the top three predictors and their weight values were systolic blood pressure (w=0.3346), age (w=0.1437) and diastolic blood pressure (w=0.1236). Factors of daily living such as education level, activity, heart rate and fish intake also showed importance. ANN can apply non-invasive data to well predict the risk of secondary hypertension in diabetic patients. Healthcare providers could use this model to rapidly screen high-risk patients and instruct them to monitor blood pressure regularly and maintain a healthy lifestyle, thereby reducing the risk of hypertension in diabetic patients. The model is more suitable for areas with high morbidity of hypertension and poor socioeconomic conditions. It has important implications for achieving health management in a larger target population, in line with the international mission to improve the global burden of hypertension. Hypertension (dpeaa)DE-He213 Diabetes (dpeaa)DE-He213 Machine learning (dpeaa)DE-He213 Prediction model (dpeaa)DE-He213 Han, Jiashu verfasserin aut Hu, Xinlin verfasserin aut Hu, Bo verfasserin aut Zhu, Hui verfasserin aut Wang, Yanlong verfasserin aut Zhu, Xiuli verfasserin (orcid)0000-0003-3533-8581 aut Enthalten in Multimedia tools and applications Springer US, 1995 83(2023), 20 vom: 28. Dez., Seite 59085-59102 (DE-627)27135030X (DE-600)1479928-5 1573-7721 nnns volume:83 year:2023 number:20 day:28 month:12 pages:59085-59102 https://dx.doi.org/10.1007/s11042-023-17926-x X:SPRINGER Resolving-System lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER SSG-OPC-BBI 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_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 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_2008 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_2119 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_2188 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_2548 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 54.87 VZ AR 83 2023 20 28 12 59085-59102 |
| spelling |
10.1007/s11042-023-17926-x doi (DE-627)SPR05609809X (SPR)s11042-023-17926-x-e DE-627 ger DE-627 rakwb eng 070 004 VZ 54.87 bkl Zhao, Yuxue verfasserin (orcid)0000-0003-1280-5298 aut Hypertension risk prediction models for patients with diabetes based on machine learning approaches 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr 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 To construct effective prediction models for hypertension in diabetic patients based on machine learning. This study used electronic data from 2080 diabetic patients attending the specialized outpatient clinic for metabolic diseases of the Affiliated Hospital of Qingdao University from March 2017 to July 2020. We adopted 5 machine learning algorithms (artificial neural network, decision tree, random forest, support vector machine, Bayesian network) and constructed hypertension risk prediction models based on patients’ non-invasive variables. The study showed that artificial neural network (ANN) performed best, accuracy, sensitivity, specificity, and area under the receiver curve in the test set were 92.47%, 92.98%, 92.02%, 0.951, respectively. The prediction model showed that the top three predictors and their weight values were systolic blood pressure (w=0.3346), age (w=0.1437) and diastolic blood pressure (w=0.1236). Factors of daily living such as education level, activity, heart rate and fish intake also showed importance. ANN can apply non-invasive data to well predict the risk of secondary hypertension in diabetic patients. Healthcare providers could use this model to rapidly screen high-risk patients and instruct them to monitor blood pressure regularly and maintain a healthy lifestyle, thereby reducing the risk of hypertension in diabetic patients. The model is more suitable for areas with high morbidity of hypertension and poor socioeconomic conditions. It has important implications for achieving health management in a larger target population, in line with the international mission to improve the global burden of hypertension. Hypertension (dpeaa)DE-He213 Diabetes (dpeaa)DE-He213 Machine learning (dpeaa)DE-He213 Prediction model (dpeaa)DE-He213 Han, Jiashu verfasserin aut Hu, Xinlin verfasserin aut Hu, Bo verfasserin aut Zhu, Hui verfasserin aut Wang, Yanlong verfasserin aut Zhu, Xiuli verfasserin (orcid)0000-0003-3533-8581 aut Enthalten in Multimedia tools and applications Springer US, 1995 83(2023), 20 vom: 28. Dez., Seite 59085-59102 (DE-627)27135030X (DE-600)1479928-5 1573-7721 nnns volume:83 year:2023 number:20 day:28 month:12 pages:59085-59102 https://dx.doi.org/10.1007/s11042-023-17926-x X:SPRINGER Resolving-System lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER SSG-OPC-BBI 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_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 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_2008 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_2119 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_2188 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_2548 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 54.87 VZ AR 83 2023 20 28 12 59085-59102 |
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10.1007/s11042-023-17926-x doi (DE-627)SPR05609809X (SPR)s11042-023-17926-x-e DE-627 ger DE-627 rakwb eng 070 004 VZ 54.87 bkl Zhao, Yuxue verfasserin (orcid)0000-0003-1280-5298 aut Hypertension risk prediction models for patients with diabetes based on machine learning approaches 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr 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 To construct effective prediction models for hypertension in diabetic patients based on machine learning. This study used electronic data from 2080 diabetic patients attending the specialized outpatient clinic for metabolic diseases of the Affiliated Hospital of Qingdao University from March 2017 to July 2020. We adopted 5 machine learning algorithms (artificial neural network, decision tree, random forest, support vector machine, Bayesian network) and constructed hypertension risk prediction models based on patients’ non-invasive variables. The study showed that artificial neural network (ANN) performed best, accuracy, sensitivity, specificity, and area under the receiver curve in the test set were 92.47%, 92.98%, 92.02%, 0.951, respectively. The prediction model showed that the top three predictors and their weight values were systolic blood pressure (w=0.3346), age (w=0.1437) and diastolic blood pressure (w=0.1236). Factors of daily living such as education level, activity, heart rate and fish intake also showed importance. ANN can apply non-invasive data to well predict the risk of secondary hypertension in diabetic patients. Healthcare providers could use this model to rapidly screen high-risk patients and instruct them to monitor blood pressure regularly and maintain a healthy lifestyle, thereby reducing the risk of hypertension in diabetic patients. The model is more suitable for areas with high morbidity of hypertension and poor socioeconomic conditions. It has important implications for achieving health management in a larger target population, in line with the international mission to improve the global burden of hypertension. Hypertension (dpeaa)DE-He213 Diabetes (dpeaa)DE-He213 Machine learning (dpeaa)DE-He213 Prediction model (dpeaa)DE-He213 Han, Jiashu verfasserin aut Hu, Xinlin verfasserin aut Hu, Bo verfasserin aut Zhu, Hui verfasserin aut Wang, Yanlong verfasserin aut Zhu, Xiuli verfasserin (orcid)0000-0003-3533-8581 aut Enthalten in Multimedia tools and applications Springer US, 1995 83(2023), 20 vom: 28. Dez., Seite 59085-59102 (DE-627)27135030X (DE-600)1479928-5 1573-7721 nnns volume:83 year:2023 number:20 day:28 month:12 pages:59085-59102 https://dx.doi.org/10.1007/s11042-023-17926-x X:SPRINGER Resolving-System lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER SSG-OPC-BBI 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_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 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_2008 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_2119 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_2188 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_2548 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 54.87 VZ AR 83 2023 20 28 12 59085-59102 |
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10.1007/s11042-023-17926-x doi (DE-627)SPR05609809X (SPR)s11042-023-17926-x-e DE-627 ger DE-627 rakwb eng 070 004 VZ 54.87 bkl Zhao, Yuxue verfasserin (orcid)0000-0003-1280-5298 aut Hypertension risk prediction models for patients with diabetes based on machine learning approaches 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr 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 To construct effective prediction models for hypertension in diabetic patients based on machine learning. This study used electronic data from 2080 diabetic patients attending the specialized outpatient clinic for metabolic diseases of the Affiliated Hospital of Qingdao University from March 2017 to July 2020. We adopted 5 machine learning algorithms (artificial neural network, decision tree, random forest, support vector machine, Bayesian network) and constructed hypertension risk prediction models based on patients’ non-invasive variables. The study showed that artificial neural network (ANN) performed best, accuracy, sensitivity, specificity, and area under the receiver curve in the test set were 92.47%, 92.98%, 92.02%, 0.951, respectively. The prediction model showed that the top three predictors and their weight values were systolic blood pressure (w=0.3346), age (w=0.1437) and diastolic blood pressure (w=0.1236). Factors of daily living such as education level, activity, heart rate and fish intake also showed importance. ANN can apply non-invasive data to well predict the risk of secondary hypertension in diabetic patients. Healthcare providers could use this model to rapidly screen high-risk patients and instruct them to monitor blood pressure regularly and maintain a healthy lifestyle, thereby reducing the risk of hypertension in diabetic patients. The model is more suitable for areas with high morbidity of hypertension and poor socioeconomic conditions. It has important implications for achieving health management in a larger target population, in line with the international mission to improve the global burden of hypertension. Hypertension (dpeaa)DE-He213 Diabetes (dpeaa)DE-He213 Machine learning (dpeaa)DE-He213 Prediction model (dpeaa)DE-He213 Han, Jiashu verfasserin aut Hu, Xinlin verfasserin aut Hu, Bo verfasserin aut Zhu, Hui verfasserin aut Wang, Yanlong verfasserin aut Zhu, Xiuli verfasserin (orcid)0000-0003-3533-8581 aut Enthalten in Multimedia tools and applications Springer US, 1995 83(2023), 20 vom: 28. Dez., Seite 59085-59102 (DE-627)27135030X (DE-600)1479928-5 1573-7721 nnns volume:83 year:2023 number:20 day:28 month:12 pages:59085-59102 https://dx.doi.org/10.1007/s11042-023-17926-x X:SPRINGER Resolving-System lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER SSG-OPC-BBI 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_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 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_2008 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_2119 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_2188 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_2548 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 54.87 VZ AR 83 2023 20 28 12 59085-59102 |
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10.1007/s11042-023-17926-x doi (DE-627)SPR05609809X (SPR)s11042-023-17926-x-e DE-627 ger DE-627 rakwb eng 070 004 VZ 54.87 bkl Zhao, Yuxue verfasserin (orcid)0000-0003-1280-5298 aut Hypertension risk prediction models for patients with diabetes based on machine learning approaches 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr 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 To construct effective prediction models for hypertension in diabetic patients based on machine learning. This study used electronic data from 2080 diabetic patients attending the specialized outpatient clinic for metabolic diseases of the Affiliated Hospital of Qingdao University from March 2017 to July 2020. We adopted 5 machine learning algorithms (artificial neural network, decision tree, random forest, support vector machine, Bayesian network) and constructed hypertension risk prediction models based on patients’ non-invasive variables. The study showed that artificial neural network (ANN) performed best, accuracy, sensitivity, specificity, and area under the receiver curve in the test set were 92.47%, 92.98%, 92.02%, 0.951, respectively. The prediction model showed that the top three predictors and their weight values were systolic blood pressure (w=0.3346), age (w=0.1437) and diastolic blood pressure (w=0.1236). Factors of daily living such as education level, activity, heart rate and fish intake also showed importance. ANN can apply non-invasive data to well predict the risk of secondary hypertension in diabetic patients. Healthcare providers could use this model to rapidly screen high-risk patients and instruct them to monitor blood pressure regularly and maintain a healthy lifestyle, thereby reducing the risk of hypertension in diabetic patients. The model is more suitable for areas with high morbidity of hypertension and poor socioeconomic conditions. It has important implications for achieving health management in a larger target population, in line with the international mission to improve the global burden of hypertension. Hypertension (dpeaa)DE-He213 Diabetes (dpeaa)DE-He213 Machine learning (dpeaa)DE-He213 Prediction model (dpeaa)DE-He213 Han, Jiashu verfasserin aut Hu, Xinlin verfasserin aut Hu, Bo verfasserin aut Zhu, Hui verfasserin aut Wang, Yanlong verfasserin aut Zhu, Xiuli verfasserin (orcid)0000-0003-3533-8581 aut Enthalten in Multimedia tools and applications Springer US, 1995 83(2023), 20 vom: 28. Dez., Seite 59085-59102 (DE-627)27135030X (DE-600)1479928-5 1573-7721 nnns volume:83 year:2023 number:20 day:28 month:12 pages:59085-59102 https://dx.doi.org/10.1007/s11042-023-17926-x X:SPRINGER Resolving-System lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER SSG-OPC-BBI 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_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 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_2008 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_2119 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_2188 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_2548 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 54.87 VZ AR 83 2023 20 28 12 59085-59102 |
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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.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract To construct effective prediction models for hypertension in diabetic patients based on machine learning. This study used electronic data from 2080 diabetic patients attending the specialized outpatient clinic for metabolic diseases of the Affiliated Hospital of Qingdao University from March 2017 to July 2020. We adopted 5 machine learning algorithms (artificial neural network, decision tree, random forest, support vector machine, Bayesian network) and constructed hypertension risk prediction models based on patients’ non-invasive variables. The study showed that artificial neural network (ANN) performed best, accuracy, sensitivity, specificity, and area under the receiver curve in the test set were 92.47%, 92.98%, 92.02%, 0.951, respectively. The prediction model showed that the top three predictors and their weight values were systolic blood pressure (w=0.3346), age (w=0.1437) and diastolic blood pressure (w=0.1236). Factors of daily living such as education level, activity, heart rate and fish intake also showed importance. ANN can apply non-invasive data to well predict the risk of secondary hypertension in diabetic patients. Healthcare providers could use this model to rapidly screen high-risk patients and instruct them to monitor blood pressure regularly and maintain a healthy lifestyle, thereby reducing the risk of hypertension in diabetic patients. The model is more suitable for areas with high morbidity of hypertension and poor socioeconomic conditions. 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hypertension risk prediction models for patients with diabetes based on machine learning approaches |
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Hypertension risk prediction models for patients with diabetes based on machine learning approaches |
| abstract |
Abstract To construct effective prediction models for hypertension in diabetic patients based on machine learning. This study used electronic data from 2080 diabetic patients attending the specialized outpatient clinic for metabolic diseases of the Affiliated Hospital of Qingdao University from March 2017 to July 2020. We adopted 5 machine learning algorithms (artificial neural network, decision tree, random forest, support vector machine, Bayesian network) and constructed hypertension risk prediction models based on patients’ non-invasive variables. The study showed that artificial neural network (ANN) performed best, accuracy, sensitivity, specificity, and area under the receiver curve in the test set were 92.47%, 92.98%, 92.02%, 0.951, respectively. The prediction model showed that the top three predictors and their weight values were systolic blood pressure (w=0.3346), age (w=0.1437) and diastolic blood pressure (w=0.1236). Factors of daily living such as education level, activity, heart rate and fish intake also showed importance. ANN can apply non-invasive data to well predict the risk of secondary hypertension in diabetic patients. Healthcare providers could use this model to rapidly screen high-risk patients and instruct them to monitor blood pressure regularly and maintain a healthy lifestyle, thereby reducing the risk of hypertension in diabetic patients. The model is more suitable for areas with high morbidity of hypertension and poor socioeconomic conditions. It has important implications for achieving health management in a larger target population, in line with the international mission to improve the global burden of hypertension. © 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. |
| abstractGer |
Abstract To construct effective prediction models for hypertension in diabetic patients based on machine learning. This study used electronic data from 2080 diabetic patients attending the specialized outpatient clinic for metabolic diseases of the Affiliated Hospital of Qingdao University from March 2017 to July 2020. We adopted 5 machine learning algorithms (artificial neural network, decision tree, random forest, support vector machine, Bayesian network) and constructed hypertension risk prediction models based on patients’ non-invasive variables. The study showed that artificial neural network (ANN) performed best, accuracy, sensitivity, specificity, and area under the receiver curve in the test set were 92.47%, 92.98%, 92.02%, 0.951, respectively. The prediction model showed that the top three predictors and their weight values were systolic blood pressure (w=0.3346), age (w=0.1437) and diastolic blood pressure (w=0.1236). Factors of daily living such as education level, activity, heart rate and fish intake also showed importance. ANN can apply non-invasive data to well predict the risk of secondary hypertension in diabetic patients. Healthcare providers could use this model to rapidly screen high-risk patients and instruct them to monitor blood pressure regularly and maintain a healthy lifestyle, thereby reducing the risk of hypertension in diabetic patients. The model is more suitable for areas with high morbidity of hypertension and poor socioeconomic conditions. It has important implications for achieving health management in a larger target population, in line with the international mission to improve the global burden of hypertension. © 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 To construct effective prediction models for hypertension in diabetic patients based on machine learning. This study used electronic data from 2080 diabetic patients attending the specialized outpatient clinic for metabolic diseases of the Affiliated Hospital of Qingdao University from March 2017 to July 2020. We adopted 5 machine learning algorithms (artificial neural network, decision tree, random forest, support vector machine, Bayesian network) and constructed hypertension risk prediction models based on patients’ non-invasive variables. The study showed that artificial neural network (ANN) performed best, accuracy, sensitivity, specificity, and area under the receiver curve in the test set were 92.47%, 92.98%, 92.02%, 0.951, respectively. The prediction model showed that the top three predictors and their weight values were systolic blood pressure (w=0.3346), age (w=0.1437) and diastolic blood pressure (w=0.1236). Factors of daily living such as education level, activity, heart rate and fish intake also showed importance. ANN can apply non-invasive data to well predict the risk of secondary hypertension in diabetic patients. Healthcare providers could use this model to rapidly screen high-risk patients and instruct them to monitor blood pressure regularly and maintain a healthy lifestyle, thereby reducing the risk of hypertension in diabetic patients. The model is more suitable for areas with high morbidity of hypertension and poor socioeconomic conditions. It has important implications for achieving health management in a larger target population, in line with the international mission to improve the global burden of hypertension. © 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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Hypertension risk prediction models for patients with diabetes based on machine learning approaches |
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Han, Jiashu Hu, Xinlin Hu, Bo Zhu, Hui Wang, Yanlong Zhu, Xiuli |
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| score |
7.400943 |