{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T19:11:23Z","timestamp":1773256283292,"version":"3.50.1"},"reference-count":40,"publisher":"Emerald","issue":"4","license":[{"start":{"date-parts":[[2021,5,4]],"date-time":"2021-05-04T00:00:00Z","timestamp":1620086400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.emerald.com\/insight\/site-policies"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["GS"],"published-print":{"date-parts":[[2021,10,19]]},"abstract":"<jats:sec><jats:title content-type=\"abstract-subheading\">Purpose<\/jats:title><jats:p>This paper develops a robust hospital readmission prediction framework by combining the feature selection algorithm and machine learning (ML) classifiers. The improved feature selection is proposed by considering the uncertainty in patient's attributes that leads to the output variable.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Design\/methodology\/approach<\/jats:title><jats:p>First, data preprocessing is conducted which includes how raw data is managed. Second, the impactful features are selected through feature selection process. It started with calculating the relational grade of each patient towards readmission using grey relational analysis (GRA) and the grade is used as the target values for feature selection. Then, the influenced features are selected using the Least Absolute Shrinkage and Selection Operator (LASSO) method. This proposed method is termed as Grey-LASSO feature selection. The final task is the readmission prediction using ML classifiers.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Findings<\/jats:title><jats:p>The proposed method offered good performances with a minimum feature subset up to 54\u201365% discarded features. Multi-Layer Perceptron with Grey-LASSO gave the best performance.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Research limitations\/implications<\/jats:title><jats:p>The performance of Grey-LASSO is justified in two readmission datasets. Further research is required to examine the generalisability to other datasets.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Originality\/value<\/jats:title><jats:p>In designing the feature selection algorithm, the selection on influenced input variables was based on the integration of GRA and LASSO. Specifically, GRA is a part of the grey system theory, which was employed to analyse the relation between systems under uncertain conditions. The LASSO approach was adopted due to its ability for sparse data representation.<\/jats:p><\/jats:sec>","DOI":"10.1108\/gs-12-2020-0168","type":"journal-article","created":{"date-parts":[[2021,5,4]],"date-time":"2021-05-04T08:43:03Z","timestamp":1620117783000},"page":"796-812","source":"Crossref","is-referenced-by-count":10,"title":["Hospital readmission prediction based on improved feature selection using grey relational analysis and LASSO"],"prefix":"10.1108","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7758-5420","authenticated-orcid":false,"given":"Nor Hamizah","family":"Miswan","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7677-2865","authenticated-orcid":false,"given":"Chee Seng","family":"Chan","sequence":"additional","affiliation":[]},{"given":"Chong Guan","family":"Ng","sequence":"additional","affiliation":[]}],"member":"140","published-online":{"date-parts":[[2021,5,4]]},"reference":[{"issue":"3","key":"key2021101806272906300_ref001","first-page":"176","article-title":"Classification with class imbalance problem: a review","volume":"7","year":"2015","journal-title":"International Journal Advance Soft Computing Application"},{"key":"key2021101806272906300_ref002","first-page":"1","article-title":"Neural networks versus logistic regression for 30 days all-cause readmission prediction","volume":"9","year":"2019","journal-title":"Scientific Reports"},{"key":"key2021101806272906300_ref003","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1016\/j.cmpb.2018.06.006","article-title":"Predictive models for hospital readmission risk: a systematic review of methods","volume":"164","year":"2018","journal-title":"Computer Methods and Programs in Biomedicine"},{"issue":"4","key":"key2021101806272906300_ref004","doi-asserted-by":"crossref","first-page":"432","DOI":"10.1108\/GS-01-2019-0001","article-title":"Evaluation of healthcare service quality factors using grey relational analysis in a dialysis center","volume":"9","year":"2019","journal-title":"Grey Systems: Theory and Application"},{"key":"key2021101806272906300_ref005","first-page":"1","article-title":"Hyperopt: a python library for model selection and hyperparameter optimization","volume":"8","year":"2015","journal-title":"Computational Science and Discovery"},{"issue":"1","key":"key2021101806272906300_ref006","first-page":"103","article-title":"Defining and measuring diagnostic uncertainty in medicine: a systematic review","volume":"33","year":"2017","journal-title":"Journal of General Internal Medicine"},{"issue":"2","key":"key2021101806272906300_ref007","doi-asserted-by":"crossref","first-page":"234","DOI":"10.1108\/GS-05-2015-0028","article-title":"Grey incidence between KPIs and hospital performance","volume":"5","year":"2015","journal-title":"Grey Systems: Theory and Application"},{"issue":"4","key":"key2021101806272906300_ref008","doi-asserted-by":"crossref","first-page":"1519","DOI":"10.1016\/j.neuroimage.2010.12.028","article-title":"Penalized least squares regression methods and applications to neuroimaging","volume":"55","year":"2011","journal-title":"Neuroimage"},{"issue":"2","key":"key2021101806272906300_ref009","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1108\/GS-11-2018-0061","article-title":"Grey relation between main meteorological factors and mortality","volume":"9","year":"2019","journal-title":"Grey Systems: Theory and Application"},{"issue":"7","key":"key2021101806272906300_ref010","doi-asserted-by":"crossref","first-page":"10727","DOI":"10.1016\/j.eswa.2009.02.064","article-title":"An early software-quality classification based on improved grey relational classifier","volume":"36","year":"2009","journal-title":"Expert Systems with Applications"},{"issue":"3","key":"key2021101806272906300_ref011","first-page":"1","article-title":"Grey system theory in the study of medical tourism industry and its economic impact","volume":"17","year":"2020","journal-title":"International Journal of Environmental Research and Public Health"},{"issue":"2","key":"key2021101806272906300_ref012","doi-asserted-by":"crossref","first-page":"216","DOI":"10.1108\/GS-12-2015-0078","article-title":"Fostering risk management in healthcare units using grey systems theory","volume":"6","year":"2016","journal-title":"Grey Systems: Theory and Application"},{"key":"key2021101806272906300_ref013","doi-asserted-by":"crossref","unstructured":"Doquire, G. and Verleysen, M. 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