{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,13]],"date-time":"2026-05-13T01:01:27Z","timestamp":1778634087394,"version":"3.51.4"},"reference-count":38,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2021,1,14]],"date-time":"2021-01-14T00:00:00Z","timestamp":1610582400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>End stage renal disease (ESRD) is the last stage of chronic kidney disease that requires dialysis or a kidney transplant to survive. Many studies reported a higher risk of mortality in ESRD patients compared with patients without ESRD. In this paper, we develop a model to predict postoperative complications, major cardiac event, for patients who underwent any type of surgery. We compare several widely-used machine learning models through experiments with our collected data yellow of size 3220, and achieved F1 score of 0.797 with the random forest model. Based on experimental results, we found that features related to operation (e.g., anesthesia time, operation time, crystal, and colloid) have the biggest impact on model performance, and also found the best combination of features. We believe that this study will allow physicians to provide more appropriate therapy to the ESRD patients by providing information on potential postoperative complications.<\/jats:p>","DOI":"10.3390\/s21020544","type":"journal-article","created":{"date-parts":[[2021,1,15]],"date-time":"2021-01-15T01:33:29Z","timestamp":1610674409000},"page":"544","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["Prediction of Postoperative Complications for Patients of End Stage Renal Disease"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9441-2940","authenticated-orcid":false,"given":"Young-Seob","family":"Jeong","sequence":"first","affiliation":[{"name":"Department of Future Convergence Technology, Soonchunhyang University, Asan-si 31538, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Juhyun","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Big Data Engineering, Soonchunhyang University, Asan-si 31538, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dahye","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Future Convergence Technology, Soonchunhyang University, Asan-si 31538, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8231-0018","authenticated-orcid":false,"given":"Jiyoung","family":"Woo","sequence":"additional","affiliation":[{"name":"Department of Future Convergence Technology, Soonchunhyang University, Asan-si 31538, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1992-8215","authenticated-orcid":false,"given":"Mun Gyu","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Anesthesiology and Pain Medicine, Soonchunhyang University Hospital Seoul, Seoul 04401, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hun Woo","family":"Choi","sequence":"additional","affiliation":[{"name":"Department of Anesthesiology and Pain Medicine, Soonchunhyang University Hospital Seoul, Seoul 04401, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0732-5313","authenticated-orcid":false,"given":"Ah Reum","family":"Kang","sequence":"additional","affiliation":[{"name":"SCH Convergence Science Institute, Soonchunhyang University, Asan-si 31538, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2588-3324","authenticated-orcid":false,"given":"Sun Young","family":"Park","sequence":"additional","affiliation":[{"name":"Department of Anesthesiology and Pain Medicine, Soonchunhyang University Hospital Seoul, Seoul 04401, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,1,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Dunn, C.P., Emeasoba, E.U., Holtzman, A.J., Hung, M., Kaminetsky, J., Alani, O., and Greenstein, S.M. 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