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In this study, we develop a machine learning-based method for predicting the discharge status of sepsis patients, aiming to improve treatment decisions. To enhance the robustness of our analysis against outliers, we incorporate robust statistical methods, specifically the minimum covariance determinant technique. We utilize the random forest imputation method to effectively manage and impute missing data. For feature selection, we employ Lasso penalized logistic regression, which efficiently identifies significant predictors and reduces model complexity, setting the stage for the application of more complex predictive methods. Our predictive analysis incorporates multiple machine learning methods, including random forest, support vector machine, and XGBoost. We compare the prediction performance of these methods with Lasso penalized logistic regression to identify the most effective approach. Each method\u2019s performance is rigorously evaluated through ten iterations of 10-fold cross-validation to ensure robust and reliable results. Our comparative analysis reveals that XGBoost surpasses the other models, demonstrating its exceptional capability to navigate the complexities of sepsis data effectively.<\/jats:p>","DOI":"10.3390\/e26080625","type":"journal-article","created":{"date-parts":[[2024,7,25]],"date-time":"2024-07-25T08:40:04Z","timestamp":1721896804000},"page":"625","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Machine Learning-Based Risk Prediction of Discharge Status for Sepsis"],"prefix":"10.3390","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2262-3869","authenticated-orcid":false,"given":"Kaida","family":"Cai","sequence":"first","affiliation":[{"name":"School of Public Health, Southeast University, Nanjing 210009, China"},{"name":"School of Mathematics, Southeast University, Nanjing 210009, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuqing","family":"Lou","sequence":"additional","affiliation":[{"name":"School of Mathematics, Southeast University, Nanjing 210009, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhengyan","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Mathematics, Southeast University, Nanjing 210009, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaofang","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Mathematics, Southeast University, Nanjing 210009, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xin","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Mathematics, Southeast University, Nanjing 210009, China"},{"name":"Key Laboratory of Measurement and Control of Complex Systems of Engineering, Ministry of Education, Southeast University, Nanjing 210096, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,7,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"801","DOI":"10.1001\/jama.2016.0287","article-title":"The third international consensus definitions for sepsis and septic shock (sepsis-3)","volume":"315","author":"Singer","year":"2016","journal-title":"J. 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