{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,8,28]],"date-time":"2024-08-28T07:16:39Z","timestamp":1724829399967},"reference-count":33,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2020,6,30]],"date-time":"2020-06-30T00:00:00Z","timestamp":1593475200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,6,30]],"date-time":"2020-06-30T00:00:00Z","timestamp":1593475200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100008628","name":"Meity","doi-asserted-by":"crossref","award":["PhD-MLA\/4(16)\/2014"],"id":[{"id":"10.13039\/501100008628","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Ambient Intell Human Comput"],"published-print":{"date-parts":[[2021,2]]},"DOI":"10.1007\/s12652-020-02250-1","type":"journal-article","created":{"date-parts":[[2020,6,30]],"date-time":"2020-06-30T04:53:23Z","timestamp":1593492803000},"page":"1771-1781","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Clustering based imputation algorithm using unsupervised neural network for enhancing the quality of healthcare data"],"prefix":"10.1007","volume":"12","author":[{"ORCID":"http:\/\/orcid.org\/0000-0002-6208-2705","authenticated-orcid":false,"given":"K.","family":"Shobha","sequence":"first","affiliation":[]},{"given":"Nickolas","family":"Savarimuthu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,6,30]]},"reference":[{"key":"2250_CR1","doi-asserted-by":"crossref","unstructured":"Almeida RJ, Kaymak U, Sousa JM (2010) A new approach to dealing with missing values in data-driven fuzzy modeling. In: International conference on fuzzy systems, pp. 1\u20137. IEEE","DOI":"10.1109\/FUZZY.2010.5584894"},{"key":"2250_CR2","doi-asserted-by":"publisher","DOI":"10.1201\/9781315156026","volume-title":"The internet of things: foundation for smart cities, EHealth, and ubiquitous computing","author":"R Armentano","year":"2017","unstructured":"Armentano R, Bhadoria RS, Chatterjee P, Deka GC (2017) The internet of things: foundation for smart cities, EHealth, and ubiquitous computing. CRC Press, Boca Raton"},{"issue":"3","key":"2250_CR3","doi-asserted-by":"publisher","first-page":"731","DOI":"10.1007\/s10115-015-0850-7","volume":"46","author":"S Arslanturk","year":"2016","unstructured":"Arslanturk S, Siadat M-R, Ogunyemi T, Killinger K, Diokno A (2016) Analysis of incomplete and inconsistent clinical survey data. Knowl Inform Syst 46(3):731\u2013750","journal-title":"Knowl Inform Syst"},{"key":"2250_CR4","doi-asserted-by":"crossref","unstructured":"Beaulieu-Jones BK, Moore JH (2017) Missing data imputation in the electronic health record using deeply learned autoencoders. In: Pacific Symposium on Biocomputing 2017, pp. 207\u2013218. World Scientific","DOI":"10.1142\/9789813207813_0021"},{"issue":"1","key":"2250_CR5","doi-asserted-by":"publisher","first-page":"493","DOI":"10.1007\/s11277-019-06414-x","volume":"108","author":"RS Bhadoria","year":"2019","unstructured":"Bhadoria RS, Bajpai D (2019) Stabilizing sensor data collection for control of environment-friendly clean technologies using internet of things. Wirel Personal Commun 108(1):493\u2013510","journal-title":"Wirel Personal Commun"},{"key":"2250_CR6","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4899-7687-1_6","volume-title":"Adaptive resonance theory","author":"GA Carpenter","year":"2017","unstructured":"Carpenter GA, Grossberg S (2017) Adaptive resonance theory. Springer, Berlin"},{"issue":"338","key":"2250_CR7","first-page":"473","volume":"67","author":"LS Chan","year":"1972","unstructured":"Chan LS, Dunn OJ (1972) The treatment of missing values in discriminant analysisi. the sampling experiment. J Am Stat Assoc 67(338):473\u2013477","journal-title":"J Am Stat Assoc"},{"key":"2250_CR8","doi-asserted-by":"publisher","first-page":"8869","DOI":"10.1109\/ACCESS.2017.2694446","volume":"5","author":"M Chen","year":"2017","unstructured":"Chen M, Hao Y, Hwang K, Wang L, Wang L (2017) Disease prediction by machine learning over big data from healthcare communities. Ieee Access 5:8869\u20138879","journal-title":"Ieee Access"},{"issue":"2","key":"2250_CR9","first-page":"1","volume":"1","author":"D Davis","year":"2016","unstructured":"Davis D, Rahman M (2016) Missing value imputation using stratified supervised learning for cardiovascular data. J. Inf. Data Min 1(2):1\u201313","journal-title":"J. Inf. Data Min"},{"issue":"11","key":"2250_CR10","doi-asserted-by":"publisher","first-page":"4164","DOI":"10.1118\/1.2786864","volume":"34","author":"M Elter","year":"2007","unstructured":"Elter M, Schulz-Wendtland R, Wittenberg T (2007) The prediction of breast cancer biopsy outcomes using two cad approaches that both emphasize an intelligible decision process. Med Phys 34(11):4164\u20134172","journal-title":"Med Phys"},{"issue":"Part IV","key":"2250_CR11","first-page":"185","volume":"2","author":"BL Ford","year":"1983","unstructured":"Ford BL (1983) An overview of hot-deck procedures. Incomplete Data Sample Surv 2(Part IV):185\u2013207","journal-title":"Incomplete Data Sample Surv"},{"issue":"3","key":"2250_CR12","doi-asserted-by":"publisher","first-page":"547","DOI":"10.1080\/02664763.2017.1284184","volume":"45","author":"S Haji-Maghsoudi","year":"2018","unstructured":"Haji-Maghsoudi S, Rastegari A, Garrusi B, Baneshi MR (2018) Addressing the problem of missing data in decision tree modeling. J Appl Stat 45(3):547\u2013557","journal-title":"J Appl Stat"},{"issue":"4","key":"2250_CR13","doi-asserted-by":"publisher","first-page":"315","DOI":"10.1080\/24725579.2019.1583704","volume":"9","author":"F Imani","year":"2019","unstructured":"Imani F, Cheng C, Chen R, Yang H (2019) Nested gaussian process modeling and imputation of high-dimensional incomplete data under uncertainty. IISE Trans Healthc Syst Eng 9(4):315\u2013326","journal-title":"IISE Trans Healthc Syst Eng"},{"issue":"2","key":"2250_CR14","doi-asserted-by":"publisher","first-page":"105","DOI":"10.1016\/j.artmed.2010.05.002","volume":"50","author":"JM Jerez","year":"2010","unstructured":"Jerez JM, Molina I, Garc\u00eda-Laencina PJ, Alba E, Ribelles N, Mart\u00edn M, Franco L (2010) Missing data imputation using statistical and machine learning methods in a real breast cancer problem. Artificial Intell Med 50(2):105\u2013115","journal-title":"Artificial Intell Med"},{"issue":"18","key":"2250_CR15","doi-asserted-by":"publisher","first-page":"2895","DOI":"10.1016\/j.atmosenv.2004.02.026","volume":"38","author":"H Junninen","year":"2004","unstructured":"Junninen H, Niska H, Tuppurainen K, Ruuskanen J, Kolehmainen M (2004) Methods for imputation of missing values in air quality data sets. Atmospheric Environ 38(18):2895\u20132907","journal-title":"Atmospheric Environ"},{"key":"2250_CR16","doi-asserted-by":"crossref","unstructured":"Kayal CK, Bagchi S, Dhar D, Maitra T, Chatterjee S (2019) Hepatocellular carcinoma survival prediction using deep neural network. In: Proceedings of international ethical hacking conference 2018, pp. 349\u2013358. Springer","DOI":"10.1007\/978-981-13-1544-2_28"},{"issue":"1","key":"2250_CR17","doi-asserted-by":"publisher","first-page":"366","DOI":"10.1016\/j.eswa.2006.09.004","volume":"34","author":"I Kurt","year":"2008","unstructured":"Kurt I, Ture M, Kurum AT (2008) Comparing performances of logistic regression, classification and regression tree, and neural networks for predicting coronary artery disease. Expert Syst Appl 34(1):366\u2013374","journal-title":"Expert Syst Appl"},{"key":"2250_CR18","doi-asserted-by":"crossref","unstructured":"LaFreniere D, Zulkernine F, Barber D, Martin K (2016) Using machine learning to predict hypertension from a clinical dataset. In: 2016 IEEE symposium series on computational intelligence (SSCI), pp. 1\u20137. IEEE","DOI":"10.1109\/SSCI.2016.7849886"},{"key":"2250_CR19","doi-asserted-by":"crossref","unstructured":"Mazumder RS, Bhadoria RS, Deka GC (eds) (2017) Distributed computing in big data analytics. Concepts, technologies and applications. Springer, Cham","DOI":"10.1007\/978-3-319-59834-5"},{"key":"2250_CR20","doi-asserted-by":"publisher","first-page":"185","DOI":"10.1007\/978-3-319-60543-2_8","volume-title":"Introduction to statistical methods in pathology","author":"A Momeni","year":"2018","unstructured":"Momeni A, Pincus M, Libien J (2018) Imputation and missing data. In: Introduction to statistical methods in pathology. Springer, Cham, pp 185\u2013200"},{"issue":"4","key":"2250_CR21","doi-asserted-by":"crossref","first-page":"347","DOI":"10.6339\/JDS.2004.02(4).170","volume":"2","author":"DV Nguyen","year":"2004","unstructured":"Nguyen DV, Wang N, Carroll RJ (2004) Evaluation of missing value estimation for microarray data. J Data Sci 2(4):347\u2013370","journal-title":"J Data Sci"},{"key":"2250_CR22","doi-asserted-by":"crossref","unstructured":"Penny KI, Chesney T (2006) Imputation methods to deal with missing values when data mining trauma injury data. In: 28th international conference on information technology interfaces, 2006, pp. 213\u2013218. IEEE","DOI":"10.1109\/ITI.2006.1708480"},{"key":"2250_CR23","unstructured":"Rahman MM (2014) Machine learning based data pre-processing for the purpose of medical data mining and decision support. PhD thesis, University of Hull"},{"key":"2250_CR24","volume-title":"Multiple imputation for nonresponse in surveys,","author":"DB Rubin","year":"2004","unstructured":"Rubin DB (2004) Multiple imputation for nonresponse in surveys, vol 81. Wiley, Hoboken"},{"key":"2250_CR25","doi-asserted-by":"publisher","first-page":"49","DOI":"10.1016\/j.jbi.2015.09.012","volume":"58","author":"MS Santos","year":"2015","unstructured":"Santos MS, Abreu PH, Garc\u00eda-Laencina PJ, Sim\u00e3o A, Carvalho A (2015) A new cluster-based oversampling method for improving survival prediction of hepatocellular carcinoma patients. J Biomed Inform 58:49\u201359","journal-title":"J Biomed Inform"},{"key":"2250_CR26","doi-asserted-by":"publisher","first-page":"483","DOI":"10.1007\/978-981-10-3373-5_48","volume-title":"Progress in intelligent computing techniques: theory, practice, and applications","author":"S Sen","year":"2018","unstructured":"Sen S, Das M, Chatterjee R (2018) Estimation of incomplete data in mixed dataset. In: Progress in intelligent computing techniques: theory, practice, and applications. Springer, Singapore, pp 483\u2013492"},{"key":"2250_CR27","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1007\/978-981-13-1927-3_6","volume-title":"Smart intelligent computing and applications","author":"K Shobha","year":"2019","unstructured":"Shobha K, Nickolas S (2019) Imputation of multivariate attribute values in big data. In: Smart intelligent computing and applications. Springer, Singapore, pp 53\u201360"},{"issue":"2","key":"2250_CR28","doi-asserted-by":"publisher","first-page":"466","DOI":"10.1016\/j.ejor.2018.02.016","volume":"269","author":"KY Sokat","year":"2018","unstructured":"Sokat KY, Dolinskaya IS, Smilowitz K, Bank R (2018) Incomplete information imputation in limited data environments with application to disaster response. Europ J Oper Res 269(2):466\u2013485","journal-title":"Europ J Oper Res"},{"issue":"6","key":"2250_CR29","doi-asserted-by":"publisher","first-page":"520","DOI":"10.1093\/bioinformatics\/17.6.520","volume":"17","author":"O Troyanskaya","year":"2001","unstructured":"Troyanskaya O, Cantor M, Sherlock G, Brown P, Hastie T, Tibshirani R, Botstein D, Altman RB (2001) Missing value estimation methods for dna microarrays. Bioinformatics 17(6):520\u2013525","journal-title":"Bioinformatics"},{"key":"2250_CR30","doi-asserted-by":"publisher","first-page":"575","DOI":"10.1016\/j.future.2018.07.006","volume":"89","author":"H Turabieh","year":"2018","unstructured":"Turabieh H, Salem AA, Abu-El-Rub N (2018) Dynamic l-rnn recovery of missing data in iomt applications. Future Generation Comput Syst 89:575\u2013583","journal-title":"Future Generation Comput Syst"},{"key":"2250_CR31","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1016\/j.csda.2015.04.009","volume":"90","author":"G Tutz","year":"2015","unstructured":"Tutz G, Ramzan S (2015) Improved methods for the imputation of missing data by nearest neighbor methods. Comput Stat Data Anal 90:84\u201399","journal-title":"Comput Stat Data Anal"},{"issue":"10","key":"2250_CR32","doi-asserted-by":"publisher","first-page":"1102","DOI":"10.1016\/j.jclinepi.2006.01.015","volume":"59","author":"GJ Van der Heijden","year":"2006","unstructured":"Van der Heijden GJ, Donders ART, Stijnen T, Moons KG (2006) Imputation of missing values is superior to complete case analysis and the missing-indicator method in multivariable diagnostic research: a clinical example. J Clin Epidemiol 59(10):1102\u20131109","journal-title":"J Clin Epidemiol"},{"key":"2250_CR33","doi-asserted-by":"crossref","unstructured":"Verma H, Kumar S (2019) An accurate missing data prediction method using lstm based deep learning for health care. In: Proceedings of the 20th international conference on distributed computing and networking, pp. 371\u2013376. ACM","DOI":"10.1145\/3288599.3295580"}],"container-title":["Journal of Ambient Intelligence and Humanized Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12652-020-02250-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s12652-020-02250-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12652-020-02250-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,10,30]],"date-time":"2022-10-30T22:58:45Z","timestamp":1667170725000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s12652-020-02250-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,6,30]]},"references-count":33,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2021,2]]}},"alternative-id":["2250"],"URL":"https:\/\/doi.org\/10.1007\/s12652-020-02250-1","relation":{},"ISSN":["1868-5137","1868-5145"],"issn-type":[{"value":"1868-5137","type":"print"},{"value":"1868-5145","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,6,30]]},"assertion":[{"value":"3 December 2019","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 June 2020","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 June 2020","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}