{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,19]],"date-time":"2026-01-19T09:24:18Z","timestamp":1768814658975,"version":"3.49.0"},"reference-count":40,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2021,9,24]],"date-time":"2021-09-24T00:00:00Z","timestamp":1632441600000},"content-version":"vor","delay-in-days":266,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["71701056"],"award-info":[{"award-number":["71701056"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Complexity"],"published-print":{"date-parts":[[2021,1]]},"abstract":"<jats:p>Research on clinical data sets of Alzheimer\u2019s disease can predict and develop early intervention treatment. Missing data is a common problem in medical research. Failure to deal with more missing data will reduce the efficiency of the test, resulting in information loss and result bias. To address these issues, this paper designs and implements the missing data interpolation method of mixed interpolation according to columns by combining the four methods of mean interpolation, regression interpolation, support vector machine (SVM) interpolation, and multiple interpolation. By comparing the effects of the mixed interpolation method with the above four interpolation methods and giving the comparison results, the experiment shows that the results of the mixed interpolation method under different data missing rates have better performance in terms of root mean square error (RMSE), mean absolute error (MSE), and error rate, which proves the effectiveness of the interpolation mechanism. The characteristics of different variables might lead to different interpolation strategy choices, and column\u2010by\u2010column mixed interpolation can dynamically select the best method according to the difference of features. To a certain extent, it selects the best method suitable for each feature and improves the interpolation effect of the data set as a whole, which is beneficial to the clinical study of Alzheimer\u2019s disease. In addition, in the processing of missing data, a combination of deletion method and interpolation method is adopted with reference to expert knowledge. Compared with the direct interpolation method, the data set obtained by this method is more accurate.<\/jats:p>","DOI":"10.1155\/2021\/3541516","type":"journal-article","created":{"date-parts":[[2021,9,24]],"date-time":"2021-09-24T19:26:26Z","timestamp":1632511586000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Missing Data Interpolation of Alzheimer\u2019s Disease Based on Column\u2010by\u2010Column Mixed Mode"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5697-3408","authenticated-orcid":false,"given":"Shi-di","family":"Miao","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4973-7164","authenticated-orcid":false,"given":"Si-qi","family":"Li","sequence":"additional","affiliation":[]},{"given":"Xu-yang","family":"Zheng","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4703-7367","authenticated-orcid":false,"given":"Rui-tao","family":"Wang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0703-4268","authenticated-orcid":false,"given":"Jing","family":"Li","sequence":"additional","affiliation":[]},{"given":"Si-si","family":"Ding","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3838-6960","authenticated-orcid":false,"given":"Jun-feng","family":"Ma","sequence":"additional","affiliation":[]}],"member":"311","published-online":{"date-parts":[[2021,9,24]]},"reference":[{"key":"e_1_2_9_1_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2013.05.011"},{"key":"e_1_2_9_2_2","doi-asserted-by":"publisher","DOI":"10.1007\/s12021-018-9370-4"},{"key":"e_1_2_9_3_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2011.10.015"},{"key":"e_1_2_9_4_2","doi-asserted-by":"publisher","DOI":"10.26599\/bdma.2019.9020007"},{"key":"e_1_2_9_5_2","doi-asserted-by":"publisher","DOI":"10.1001\/jamaneurol.2015.3037"},{"key":"e_1_2_9_6_2","doi-asserted-by":"publisher","DOI":"10.1001\/jamainternmed.2015.5231"},{"key":"e_1_2_9_7_2","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6560\/ab7e7d"},{"key":"e_1_2_9_8_2","first-page":"29","article-title":"Effect of cinnamomum verum extract on the amyloid formation of hen egg-white lysozyme and study of its possible role in Alzheimer\u2019s disease","volume":"6","author":"Ramshini H.","year":"2015","journal-title":"Basic and Clinical Neuroscience"},{"key":"e_1_2_9_9_2","doi-asserted-by":"crossref","unstructured":"EzzineI.andBenhlimaL. A study of handling missing data methods for big data Proceedings of the 2018 IEEE 5th International Congress on Information Science and Technology (CiSt) October 2018 Marrakech Morocco IEEE https:\/\/doi.org\/10.1109\/cist.2018.8596389 2-s2.0-85061450646.","DOI":"10.1109\/CIST.2018.8596389"},{"key":"e_1_2_9_10_2","doi-asserted-by":"crossref","unstructured":"IliouT. AnagnostopoulosC. N. andNerantzakiM. A novel machine learning data preprocessing method for enhancing classification algorithms performance Proceedings of the 16th International Conference on Engineering Applications of Neural Networks (INNS) September 2015 Halkidiki Greece 1\u20135 https:\/\/doi.org\/10.1145\/2797143.2797155.","DOI":"10.1145\/2797143.2797155"},{"key":"e_1_2_9_11_2","first-page":"1010","article-title":"Machine learning based intelligent framework for data preprocessing","volume":"15","author":"Sarwar S.","year":"2018","journal-title":"The International Arab Journal of Information Technology"},{"key":"e_1_2_9_12_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-009-0295-6"},{"key":"e_1_2_9_13_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10489-009-0207-6"},{"key":"e_1_2_9_14_2","doi-asserted-by":"publisher","DOI":"10.4172\/2155-6180.1000224"},{"key":"e_1_2_9_15_2","doi-asserted-by":"publisher","DOI":"10.1016\/s0895-4356(03)00170-7"},{"key":"e_1_2_9_16_2","doi-asserted-by":"publisher","DOI":"10.1186\/s12874-016-0188-1"},{"key":"e_1_2_9_17_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10462-021-09963-5"},{"key":"e_1_2_9_18_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11634-014-0195-1"},{"key":"e_1_2_9_19_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2020.106249"},{"key":"e_1_2_9_20_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10115-015-0822-y"},{"key":"e_1_2_9_21_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jss.2016.08.093"},{"key":"e_1_2_9_22_2","doi-asserted-by":"crossref","unstructured":"LiuX. LaiX. C. andZhangL. A hierarchical missing value imputation method by correlation-based K-nearest neighbor Intelligent Systems and Applications 1037 468\u2013496 https:\/\/doi.org\/10.1007\/978-3-030-29516-5_38.","DOI":"10.1007\/978-3-030-29516-5_38"},{"key":"e_1_2_9_23_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.patrec.2015.08.023"},{"key":"e_1_2_9_24_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ymssp.2017.01.018"},{"key":"e_1_2_9_25_2","doi-asserted-by":"publisher","DOI":"10.1109\/JSEN.2020.3033153"},{"key":"e_1_2_9_26_2","doi-asserted-by":"publisher","DOI":"10.1109\/tii.2018.2875067"},{"key":"e_1_2_9_27_2","doi-asserted-by":"crossref","unstructured":"MaryI. S. P.andArockiamL. Imputing the missing data in loT based on the spatial and temporal correlation Proceedings of the IEEE International Conference on Current Trends in Advanced Computing (ICCTAC) 2017 Bangalore India.","DOI":"10.1109\/ICCTAC.2017.8249990"},{"key":"e_1_2_9_28_2","doi-asserted-by":"publisher","DOI":"10.1109\/tmi.2019.2913158"},{"key":"e_1_2_9_29_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2020.101953"},{"key":"e_1_2_9_30_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2013.08.015"},{"key":"e_1_2_9_31_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46720-7_36"},{"key":"e_1_2_9_32_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2019.101630"},{"key":"e_1_2_9_33_2","doi-asserted-by":"publisher","DOI":"10.3389\/fnins.2021.634124"},{"key":"e_1_2_9_34_2","doi-asserted-by":"publisher","DOI":"10.1109\/jbhi.2017.2732287"},{"key":"e_1_2_9_35_2","doi-asserted-by":"publisher","DOI":"10.1109\/tmi.2020.2983085"},{"key":"e_1_2_9_36_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-03991-2_33"},{"key":"e_1_2_9_37_2","doi-asserted-by":"crossref","unstructured":"LsilvaE. RafaeP. M. andManuelL. C. Single imputation with multilayer perceptron and multiple imputation combining multilayer perceptron and K-nearest neighbours for monotone patterns Applied Soft Computing 29 no. 9 65\u201374 https:\/\/doi.org\/10.1016\/j.asoc.2014.09.052 2-s2.0-84920696294.","DOI":"10.1016\/j.asoc.2014.09.052"},{"key":"e_1_2_9_38_2","doi-asserted-by":"publisher","DOI":"10.1142\/s0218348x19500555"},{"key":"e_1_2_9_39_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11222-019-09857-1"},{"key":"e_1_2_9_40_2","unstructured":"http:\/\/adni.loni.usc.edu\/."}],"container-title":["Complexity"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/downloads.hindawi.com\/journals\/complexity\/2021\/3541516.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/complexity\/2021\/3541516.xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1155\/2021\/3541516","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,9]],"date-time":"2024-08-09T21:50:32Z","timestamp":1723240232000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1155\/2021\/3541516"}},"subtitle":[],"editor":[{"given":"Daniele","family":"Salvati","sequence":"additional","affiliation":[]}],"short-title":[],"issued":{"date-parts":[[2021,1]]},"references-count":40,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2021,1]]}},"alternative-id":["10.1155\/2021\/3541516"],"URL":"https:\/\/doi.org\/10.1155\/2021\/3541516","archive":["Portico"],"relation":{},"ISSN":["1076-2787","1099-0526"],"issn-type":[{"value":"1076-2787","type":"print"},{"value":"1099-0526","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,1]]},"assertion":[{"value":"2021-05-14","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2021-09-03","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2021-09-24","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"3541516"}}