{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,5]],"date-time":"2026-02-05T09:19:26Z","timestamp":1770283166254,"version":"3.49.0"},"reference-count":10,"publisher":"Wiley","license":[{"start":{"date-parts":[[2017,1,1]],"date-time":"2017-01-01T00:00:00Z","timestamp":1483228800000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Program of Scientific and Technological Research of Henan Province"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Journal of Healthcare Engineering"],"published-print":{"date-parts":[[2017]]},"abstract":"<jats:p>It is important to identify and prevent disease risk as early as possible through regular physical examinations. We formulate the disease risk prediction into a multilabel classification problem. A novel Ensemble Label Power-set Pruned datasets Joint Decomposition (ELPPJD) method is proposed in this work. First, we transform the multilabel classification into a multiclass classification. Then, we propose the pruned datasets and joint decomposition methods to deal with the imbalance learning problem. Two strategies size balanced (SB) and label similarity (LS) are designed to decompose the training dataset. In the experiments, the dataset is from the real physical examination records. We contrast the performance of the ELPPJD method with two different decomposition strategies. Moreover, the comparison between ELPPJD and the classic multilabel classification methods RAkEL and HOMER is carried out. The experimental results show that the ELPPJD method with label similarity strategy has outstanding performance.<\/jats:p>","DOI":"10.1155\/2017\/8051673","type":"journal-article","created":{"date-parts":[[2017,6,15]],"date-time":"2017-06-15T17:28:34Z","timestamp":1497547714000},"page":"1-10","source":"Crossref","is-referenced-by-count":24,"title":["An Ensemble Multilabel Classification for Disease Risk Prediction"],"prefix":"10.1155","volume":"2017","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7259-9321","authenticated-orcid":true,"given":"Runzhi","family":"Li","sequence":"first","affiliation":[{"name":"Cooperative Innovation Center of Internet Healthcare, Zhengzhou University, Zhengzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wei","family":"Liu","sequence":"additional","affiliation":[{"name":"Cooperative Innovation Center of Internet Healthcare, Zhengzhou University, Zhengzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2284-5763","authenticated-orcid":true,"given":"Yusong","family":"Lin","sequence":"additional","affiliation":[{"name":"Cooperative Innovation Center of Internet Healthcare, Zhengzhou University, Zhengzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hongling","family":"Zhao","sequence":"additional","affiliation":[{"name":"Cooperative Innovation Center of Internet Healthcare, Zhengzhou University, Zhengzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chaoyang","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Computing, University of Southern Mississippi, Hattiesburg, MS, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","reference":[{"key":"1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2011.01.114"},{"key":"5","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2004.03.009"},{"issue":"2","key":"7","first-page":"135","volume":"39","year":"2000","journal-title":"Machine Learning"},{"key":"9","doi-asserted-by":"publisher","DOI":"10.4018\/jdwm.2007070101"},{"issue":"4","key":"10","first-page":"975","volume":"5","year":"2004","journal-title":"Journal of Machine Learning Research"},{"issue":"7","key":"12","first-page":"1079","volume":"23","year":"2010","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"issue":"3","key":"14","first-page":"254","volume":"85","year":"2011","journal-title":"Machine Learning"},{"key":"15","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2012.03.004"},{"key":"17","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","volume":"45","year":"2001","journal-title":"Machine Learning"},{"key":"18","year":"2014"}],"container-title":["Journal of Healthcare Engineering"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/downloads.hindawi.com\/journals\/jhe\/2017\/8051673.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/jhe\/2017\/8051673.xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/jhe\/2017\/8051673.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2017,6,15]],"date-time":"2017-06-15T17:28:38Z","timestamp":1497547718000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.hindawi.com\/journals\/jhe\/2017\/8051673\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017]]},"references-count":10,"alternative-id":["8051673","8051673"],"URL":"https:\/\/doi.org\/10.1155\/2017\/8051673","relation":{},"ISSN":["2040-2295","2040-2309"],"issn-type":[{"value":"2040-2295","type":"print"},{"value":"2040-2309","type":"electronic"}],"subject":[],"published":{"date-parts":[[2017]]}}}