{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T20:09:04Z","timestamp":1743019744321,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":21,"publisher":"Springer Singapore","isbn-type":[{"type":"print","value":"9789811557873"},{"type":"electronic","value":"9789811557880"}],"license":[{"start":{"date-parts":[[2020,9,9]],"date-time":"2020-09-09T00:00:00Z","timestamp":1599609600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,9,9]],"date-time":"2020-09-09T00:00:00Z","timestamp":1599609600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-981-15-5788-0_57","type":"book-chapter","created":{"date-parts":[[2020,9,8]],"date-time":"2020-09-08T08:02:45Z","timestamp":1599552165000},"page":"599-610","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Hypertension Risk Prediction Using Deep Neural Network"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6516-6368","authenticated-orcid":false,"given":"M. J.","family":"Sivambigai","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0652-4262","authenticated-orcid":false,"given":"E.","family":"Murugavalli","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,9,9]]},"reference":[{"key":"57_CR1","doi-asserted-by":"crossref","unstructured":"Sood, S.K., Maharajan, I.: IoT-fog-based healthcare framework to identify and control hypertension attack. IEEE Internet Things J. 6(2) (2019)","DOI":"10.1109\/JIOT.2018.2871630"},{"key":"57_CR2","unstructured":"Mendis, S.: Global status report on non-communicable diseases 2010. Tech. Rep., World Health Organisation (2010)"},{"key":"57_CR3","doi-asserted-by":"crossref","unstructured":"Tabrizi, J.S., Sadeghi-Bazargani, H., Farahbaksh, M., Nikniaz, L., Nikniaz, Z.: Prevalence and associated factors of prehypertension and hypertension in Iranian population: the lifestyle promotion project (LPP). PLoS ONE 11(10), Article ID e0165264 (2016)","DOI":"10.1371\/journal.pone.0165264"},{"key":"57_CR4","first-page":"65","volume":"10","author":"R Gupta","year":"2006","unstructured":"Gupta, R.: Rethinking diseases of affluence: coronary heart disease in developing countries. South Asian J. Prev. Cardiol. 10, 65\u201378 (2006)","journal-title":"South Asian J. Prev. Cardiol."},{"key":"57_CR5","doi-asserted-by":"crossref","unstructured":"Wu, J., Zhang, L., Yin, S., Wang, H., Wang, G., Yuan, J.: Differential diagnosis model of hypocellular myelodysplastic syndrome and aplastic anemia based on the medical big data platform. Complexity 2018, Article no. 4824350 (2018)","DOI":"10.1155\/2018\/4824350"},{"key":"57_CR6","doi-asserted-by":"crossref","unstructured":"Lui, Y.W., et al.: Classification algorithms using multiple MRI features in mild traumatic brain injury. Neurology 83(14), 1235\u20131240 (2014)","DOI":"10.1212\/WNL.0000000000000834"},{"issue":"5","key":"57_CR7","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10916-015-0241-3","volume":"39","author":"W-T Tseng","year":"2015","unstructured":"Tseng, W.-T., Chiang, W.-F., Liu, S.-Y., Roan, J., Lin, C.-N.: The application of data mining techniques to oral cancer prognosis. J. Med. Syst. 39(5), 1\u20137 (2015)","journal-title":"J. Med. Syst."},{"key":"57_CR8","doi-asserted-by":"crossref","unstructured":"Hung, C.-Y., Chen, W.-C., Lai, P.-T., Lin, C.-H., Lee, C.-C.: Comparing deep neural network and other machine learning algorithms for stroke prediction in a large-scale population-based electronic medical claims database. In: 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3110\u20133113 (2017)","DOI":"10.1109\/EMBC.2017.8037515"},{"key":"57_CR9","doi-asserted-by":"crossref","unstructured":"Singh, D., et al.: Human activity recognition using recurrent neural networks. In: Machine Learning and Knowledge Extraction. Lecture Notes in Computer Science, vol. 10410, pp. 267\u2013274 (2017)","DOI":"10.1007\/978-3-319-66808-6_18"},{"key":"57_CR10","doi-asserted-by":"crossref","unstructured":"LaFreniere, D., Zulkernine, F., Barber, D., Martin, K.: Using machine learning to predict hypertension from a clinical dataset. In: 2016 IEEE Symposium Series on Computational Intelligence (SSCI) (2016)","DOI":"10.1109\/SSCI.2016.7849886"},{"key":"57_CR11","doi-asserted-by":"crossref","unstructured":"Quachtran, B., Hamilton, R., Scalzo, F.: Detection of intracranial hypertension using deep learning. In: 2016 23rd International Conference on Pattern Recognition (ICPR), pp. 2491\u20132496 (2016)","DOI":"10.1109\/ICPR.2016.7900010"},{"key":"57_CR12","doi-asserted-by":"crossref","unstructured":"Chai, S., Wu, L.Y., Chang, S.-T., Lin, C.-J., Liu, Y.-T.: Establish a Predictive Model of Hypertension Complications, vol. 2, pp. 515\u2013520 (2018)","DOI":"10.1109\/ICMLC.2018.8526951"},{"key":"57_CR13","doi-asserted-by":"crossref","unstructured":"Wu, J.-H., Wei, W., Zhang, L., Wang, J., Robertas, D., Jing, L., Wang, H.-D., Wang, G.-L., Zhang, X., Yuan, J.-X., Wozniak, M.: Risk assessment of hypertension in steel workers based on LVQ and Fisher-SVM deep excavation. IEEE Access 7, 23109\u201323119 (2019)","DOI":"10.1109\/ACCESS.2019.2899625"},{"key":"57_CR14","unstructured":"Available at \nhttps:\/\/www.kaggle.com\/asaumya\/patient-data-train-and-test-set\n\n and \nhttps:\/\/www.kaggle.com\/navink25\/framingham"},{"key":"57_CR15","unstructured":"American Heart Association: Understanding Blood Pressure Readings (2011)"},{"key":"57_CR16","unstructured":"American Heart Association: Good vs. Bad Cholesterol (2009)"},{"key":"57_CR17","doi-asserted-by":"crossref","unstructured":"Sparrow, D., Garvey, A.J., Rosner, Jr., B., Thomas, H.E.: Factors in predicting blood pressure change. J. Circ. 65, 789\u2013794 (1982)","DOI":"10.1161\/01.CIR.65.4.789"},{"key":"57_CR18","unstructured":"American Heart Association: Prevention and Treatment: Tobacco and Blood Pressure (2014)"},{"key":"57_CR19","doi-asserted-by":"crossref","unstructured":"Omvik, P.: How smoking affects blood pressure. Blood Press. 5, 71\u201377 (1996)","DOI":"10.3109\/08037059609062111"},{"key":"57_CR20","doi-asserted-by":"crossref","unstructured":"Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Learning representations by back-propagating errors. Nature 323(6088), 533 (1986)","DOI":"10.1038\/323533a0"},{"key":"57_CR21","unstructured":"Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning, vol. 1. MIT Press, Cambridge (2016)"}],"container-title":["Advances in Intelligent Systems and Computing","Evolution in Computational Intelligence"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-15-5788-0_57","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,9,8]],"date-time":"2020-09-08T08:13:42Z","timestamp":1599552822000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-981-15-5788-0_57"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,9,9]]},"ISBN":["9789811557873","9789811557880"],"references-count":21,"URL":"https:\/\/doi.org\/10.1007\/978-981-15-5788-0_57","relation":{},"ISSN":["2194-5357","2194-5365"],"issn-type":[{"type":"print","value":"2194-5357"},{"type":"electronic","value":"2194-5365"}],"subject":[],"published":{"date-parts":[[2020,9,9]]},"assertion":[{"value":"9 September 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}}]}}