| Literature DB >> 33466610 |
Young-Seob Jeong1, Juhyun Kim2, Dahye Kim1, Jiyoung Woo1, Mun Gyu Kim3, Hun Woo Choi3, Ah Reum Kang4, Sun Young Park3.
Abstract
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.Entities:
Keywords: end stage renal disease; feature selection; machine learning model; postoperative complication; postoperative complications
Mesh:
Year: 2021 PMID: 33466610 PMCID: PMC7828737 DOI: 10.3390/s21020544
Source DB: PubMed Journal: Sensors (Basel) ISSN: 1424-8220 Impact factor: 3.576