{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,14]],"date-time":"2026-07-14T17:45:49Z","timestamp":1784051149459,"version":"3.55.0"},"reference-count":30,"publisher":"Springer Science and Business Media LLC","issue":"20","license":[{"start":{"date-parts":[[2021,5,20]],"date-time":"2021-05-20T00:00:00Z","timestamp":1621468800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2021,5,20]],"date-time":"2021-05-20T00:00:00Z","timestamp":1621468800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput & Applic"],"published-print":{"date-parts":[[2023,7]]},"DOI":"10.1007\/s00521-021-06060-0","type":"journal-article","created":{"date-parts":[[2021,5,20]],"date-time":"2021-05-20T08:25:41Z","timestamp":1621499141000},"page":"14487-14497","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":52,"title":["A hybrid machine learning approach for hypertension risk prediction"],"prefix":"10.1007","volume":"35","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8798-537X","authenticated-orcid":false,"given":"Min","family":"Fang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yingru","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rui","family":"Xue","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huihui","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nilesh","family":"Chakraborty","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ting","family":"Su","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuyan","family":"Dai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2021,5,20]]},"reference":[{"issue":"6","key":"6060_CR1","doi-asserted-by":"publisher","first-page":"441","DOI":"10.1161\/CIRCULATIONAHA.115.018912","volume":"134","author":"KT Mills","year":"2016","unstructured":"Mills KT, Bundy JD, Kelly TN, Reed JE, Kearney PM (2016) Global disparities of hypertension prevalence and control: a systematic analysis of population-based studies from 90 countries. Circulation 134(6):441\u2013450","journal-title":"Circulation"},{"key":"6060_CR2","first-page":"24","volume":"24","author":"L Liu","year":"2019","unstructured":"Liu L (2019) 2018 Chinese guidelines for the management of hypertension. Chin J Cardiovasc Med 24:24\u201356","journal-title":"Chin J Cardiovasc Med"},{"key":"6060_CR3","doi-asserted-by":"publisher","unstructured":"Liu L (2011) Writing group of Chinese guidelines for the management of H: 2010 Chinese guidelines for the management of hypertension [Article in Chinese]. Zhonghua xin xue guan bing za zhi 39:579\u2013615. https:\/\/doi.org\/10.1038\/cdd.2010.68","DOI":"10.1038\/cdd.2010.68"},{"key":"6060_CR4","doi-asserted-by":"crossref","unstructured":"Chen ShuTang (1999) Prevention of hypertension [Article in Chinese]. China Healthcare & Nutrition 10:26\u201327. https:\/\/doi.org\/CNKI:SUN:ZHBJ.0.1999-10-018","DOI":"10.1088\/1126-6708\/1999\/10\/018"},{"key":"6060_CR5","doi-asserted-by":"crossref","unstructured":"Boudabsa L, Filipovic D (2020) Machine learning with kernels for portfolio valuation and risk management, Papers, arXiv:1906.03726. https:\/\/EconPapers.repec.org\/RePEc:arx:papers:1906.03726","DOI":"10.2139\/ssrn.3401539"},{"issue":"10","key":"6060_CR6","doi-asserted-by":"publisher","first-page":"4190","DOI":"10.1021\/acs.chemmater.6b04229","volume":"29","author":"P Pankajakshan","year":"2017","unstructured":"Pankajakshan P, Sanyal S, de Noord OE, Bhattacharya I, Bhattacharyya A, Waghmare U (2017) Machine learning and statistical analysis for materials science: stability and transferability of fingerprint descriptors and chemical insights. Chem Mater 29(10):4190\u20134201","journal-title":"Chem Mater"},{"issue":"10","key":"6060_CR7","doi-asserted-by":"publisher","first-page":"624","DOI":"10.1021\/acs.estlett.9b00476","volume":"6","author":"A Raza","year":"2019","unstructured":"Raza A, Bardhan S, Xu L, Yamijala SS, Lian C, Kwon H, Wong BM (2019) A machine learning approach for predicting defluorination of per-and Polyfluoroalkyl Substances (PFAS) for their efficient treatment and removal. Environ Sci Technol Lett 6(10):624\u2013629","journal-title":"Environ Sci Technol Lett"},{"key":"6060_CR8","first-page":"2019","volume":"01","author":"B Lotfi","year":"2019","unstructured":"Lotfi B, Damir F (2019) Machine learning with kernels for portfolio valuation and risk management. SSRN Electron J 01:2019","journal-title":"SSRN Electron J"},{"key":"6060_CR9","doi-asserted-by":"crossref","unstructured":"Song H,  Srinivasan R,  Sookoor T, Jeschke S (2017) Smart cities: foundations, principles and applications.  Wiley, Hoboken, NJ, pp 1\u2013906. ISBN: 978-1-119-22639-0","DOI":"10.1002\/9781119226444"},{"key":"6060_CR10","volume-title":"Industrial internet of things","author":"J Sabina","year":"2017","unstructured":"Sabina J, Christian B, Houbing S, Rawat DB (2017) Industrial internet of things. Springer International Publishing, New York"},{"key":"6060_CR11","first-page":"766","volume":"4","author":"S Yunchuan","year":"2017","unstructured":"Yunchuan S, Houbing S, Jara AJ, Rongfang B (2017) Internet of things and big data analytics for smart and connected communities. IEEE Access 4:766\u2013773","journal-title":"IEEE Access"},{"issue":"4","key":"6060_CR12","doi-asserted-by":"publisher","first-page":"311","DOI":"10.1109\/JIOT.2014.2329462","volume":"1","author":"Z Yuan","year":"2014","unstructured":"Yuan Z, Limin S, Houbing S, Xiaojun C (2014) Ubiquitous WSN for healthcare: recent advances and future prospects. IEEE Internet Things J 1(4):311\u2013318","journal-title":"IEEE Internet Things J"},{"issue":"99","key":"6060_CR13","doi-asserted-by":"publisher","first-page":"6171","DOI":"10.1109\/ACCESS.2016.2613278","volume":"4","author":"AT Lo\u2019Ai","year":"2017","unstructured":"Lo\u2019Ai AT, Mehmood R, Benkhelifa E, Song H (2017) Mobile cloud computing model and big data analysis for healthcare applications. IEEE Access 4(99):6171\u20136180. https:\/\/doi.org\/10.1109\/ACCESS.2016.2613278","journal-title":"IEEE Access"},{"key":"6060_CR14","doi-asserted-by":"publisher","unstructured":"Tawalbeh LA, Bakhader W, Mehmood R, Song H (2016) Cloudlet-based mobile cloud computing for healthcare applications. 2016 IEEE Global Communications Conference (GLOBECOM), Washington, DC, pp 1\u20136. https:\/\/doi.org\/10.1109\/GLOCOM.2016.7841665","DOI":"10.1109\/GLOCOM.2016.7841665"},{"issue":"2","key":"6060_CR15","doi-asserted-by":"publisher","first-page":"1375","DOI":"10.1109\/JIOT.2018.2842229","volume":"6","author":"B Jiang","year":"2018","unstructured":"Jiang B, Yang J, Lv Z, Song H (2018) Wearable vision assistance system based on binocular sensors for visually impaired users. IEEE Internet Things J 6(2):1375\u20131383","journal-title":"IEEE Internet Things J"},{"issue":"4","key":"6060_CR16","doi-asserted-by":"publisher","first-page":"1900","DOI":"10.1109\/TII.2016.2573762","volume":"13","author":"Y Jiang","year":"2016","unstructured":"Jiang Y, Song H, Wang R, Gu M, Sun J, Sha L (2016) Data-centered runtime verification of wireless medical cyber-physical system. IEEE Trans Ind Inform 13(4):1900\u20131909","journal-title":"IEEE Trans Ind Inform"},{"key":"6060_CR17","doi-asserted-by":"publisher","first-page":"14","DOI":"10.3389\/fninf.2014.00014","volume":"8","author":"A Abraham","year":"2014","unstructured":"Abraham A, Pedregosa F, Eickenberg M, Gervais P, Mueller A, Kossaifi J, Varoquaux G (2014) Machine learning for neuroimaging with scikit-learn. Front Neuroinform 8:14","journal-title":"Front Neuroinform"},{"issue":"10","key":"6060_CR18","doi-asserted-by":"publisher","first-page":"1666","DOI":"10.1093\/bioinformatics\/btx801","volume":"34","author":"Y Yang","year":"2018","unstructured":"Yang Y, Niehaus KE, Walker TM, Iqbal Z, Walker AS, Wilson DJ, Peto TE, Crook DW, Smith EG, Zhu T (2018) Machine learning for classifying tuberculosis drug-resistance from DNA sequencing data. Bioinformatics 34(10):1666\u20131671","journal-title":"Bioinformatics"},{"key":"6060_CR19","doi-asserted-by":"publisher","unstructured":"Hazan H, Hilu D, Manevitz L, Ramig LO, Sapir S (2012) Early diagnosis of Parkinson's disease via machine learning on speech data. 2012 IEEE 27th Convention of Electrical and Electronics Engineers in Israel, pp 1\u20134. https:\/\/doi.org\/10.1109\/EEEI.2012.6377065","DOI":"10.1109\/EEEI.2012.6377065"},{"issue":"2","key":"6060_CR20","doi-asserted-by":"publisher","first-page":"45","DOI":"10.24171\/j.phrp.2018.9.2.02","volume":"9","author":"SH Park","year":"2018","unstructured":"Park SH, Kim SG (2018) Comparison of hypertension prediction analysis using waist measurement and body mass index by age group. Osong Public Health Res Perspect 9(2):45\u201349","journal-title":"Osong Public Health Res Perspect"},{"issue":"8","key":"6060_CR21","doi-asserted-by":"publisher","first-page":"1325","DOI":"10.3390\/app8081325","volume":"8","author":"I Muhammad","year":"2018","unstructured":"Muhammad I, Ganjar A, Muhammad S, Jongtae R (2018) Hybrid prediction model for type 2 diabetes and hypertension using DBSCAN-based outlier detection, synthetic minority over sampling technique (smote), and random forest. Appl Sci 8(8):1325","journal-title":"Appl Sci"},{"key":"6060_CR22","doi-asserted-by":"publisher","unstructured":"Wang A, An N, Xia Y, Li L, Chen G (2014) A logistic regression and artificial neural network-based approach for chronic disease prediction: a case study of hypertension. 2014 IEEE International Conference on Internet of Things (iThings), and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom), pp 45\u201352. https:\/\/doi.org\/10.1109\/iThings.2014.16","DOI":"10.1109\/iThings.2014.16"},{"key":"6060_CR23","first-page":"445","volume":"22","author":"K Hiroshi","year":"2019","unstructured":"Hiroshi K, Kenji S, Kyohei F, Tetsuya I, Kazuomi K (2019) Highly precise risk prediction model for new-onset hypertension using artificial intelligence techniques. J Clin Hypertens 22:445\u2013450","journal-title":"J Clin Hypertens"},{"key":"6060_CR24","doi-asserted-by":"publisher","unstructured":"Zhao H, Ma Z, Sun Y (2019) A hypertension risk prediction model based on BP neural network. 2019 International Conference on Networking and Network Applications (NaNA), pp 464\u2013469. https:\/\/doi.org\/10.1109\/NaNA.2019.00085","DOI":"10.1109\/NaNA.2019.00085"},{"issue":"2","key":"6060_CR25","doi-asserted-by":"publisher","first-page":"544","DOI":"10.1161\/01.HYP.35.2.544","volume":"35","author":"J He","year":"2000","unstructured":"He J, Whelton PK, Appel LJ, Charleston J, Klag MJ (2000) Long-term effects of weight loss and dietary sodium reduction on incidence of hypertension. Hypertension 35(2):544\u2013549","journal-title":"Hypertension"},{"key":"6060_CR26","doi-asserted-by":"publisher","first-page":"1988","DOI":"10.1038\/s41366-019-0384-2","volume":"43","author":"H Emilia","year":"2019","unstructured":"Emilia H, Pernilla D, Amira E, Claude M (2019) The effect of weight loss and weight gain on blood pressure in children and adolescents with obesity. Int J Obes 43:1988\u20131994","journal-title":"Int J Obes"},{"key":"6060_CR27","doi-asserted-by":"publisher","first-page":"657","DOI":"10.1001\/archinte.1997.00440270105009","volume":"157","author":"Trials of Hypertension Prevention Collaborative Research Group","year":"1997","unstructured":"Trials of Hypertension Prevention Collaborative Research Group (1997) Effects of weight loss and sodium reduction intervention on blood pressure and hypertension incidence in over-weight people with high normal blood pressure: the trials of hypertension prevention. Arch Int Med 157:657\u2013667","journal-title":"Arch Int Med"},{"issue":"1","key":"6060_CR28","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1109\/TIT.1967.1053964","volume":"13","author":"T Cover","year":"2003","unstructured":"Cover T, Hart P (2003) Nearest neighbor pattern classification. IEEE Trans Inf Theory 13(1):21\u201327","journal-title":"IEEE Trans Inf Theory"},{"key":"6060_CR29","doi-asserted-by":"publisher","unstructured":"Zanchettin C, Bezerra BLD, Azevedo WW (2012) A KNN-SVM hybrid model for cursive handwriting recognition. The 2012 International Joint Conference on Neural Networks (IJCNN), pp 1\u20138. https:\/\/doi.org\/10.1109\/IJCNN.2012.6252719","DOI":"10.1109\/IJCNN.2012.6252719"},{"key":"6060_CR30","unstructured":"Ke G, Meng Q, Finely T, Wang T, Chen W, Ma W, Ye Q, Liu T-Y (2017) Lightgbm: a highly efficient gradient boosting decision tree. In Advances in Neural Information Processing Systems 30 (NIP 2017). https:\/\/papers.nips.cc\/paper\/2017\/file\/6449f44a102fde848669bdd9eb6b76fa-Paper.pdf"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-021-06060-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-021-06060-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-021-06060-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,6,2]],"date-time":"2023-06-02T09:50:49Z","timestamp":1685699449000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-021-06060-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,5,20]]},"references-count":30,"journal-issue":{"issue":"20","published-print":{"date-parts":[[2023,7]]}},"alternative-id":["6060"],"URL":"https:\/\/doi.org\/10.1007\/s00521-021-06060-0","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,5,20]]},"assertion":[{"value":"6 December 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 April 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 May 2021","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflicts of interest"}}]}}