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Reliable early prediction is essential for timely intervention and improved outcomes. We aimed to develop and validate a machine-learning model for predicting sepsis onset beyond the first 24\u00a0h of ICU admission. Data from septic patients were extracted from the Medical Information Mart for Intensive Care III (MIMIC-III) database. Feature selection was performed with the Boruta algorithm. Nine algorithms\u2014XGBoost-DART, Gaussian Na\u00efve Bayes, LightGBM-DART, Random Forest, AdaBoost, Multi-Layer Perceptron (MLP), Support Vector Machine (SVM-RBF), k-Nearest Neighbors (KNN), and Ridge Regression\u2014were trained and comprehensively evaluated with respect to discrimination, calibration, and clinical utility. Class imbalance was addressed using SMOTE on the training set and cost-sensitive learning for applicable algorithms. Among 1,634 ICU patients included (after excluding those meeting Sepsis-3 criteria within the first 24\u00a0h), 349 (21.4%) developed sepsis after the 24-hour observation window. AUROCs ranged from 0.794 to 0.881 across the nine models. AUROCs ranged from 0.810 to 0.895 across the nine models. XGBoost-DART achieved the highest AUROC (0.881, 95% CI: 0.854\u20130.908) along with the best accuracy (0.847), F1-score (0.762), and specificity (0.897). Decision-curve analysis demonstrated that XGBoost-DART delivered the greatest net benefit over the widest range of threshold probabilities, underscoring its strong clinical utility. In summary, machine-learning models provide a reliable tool for early sepsis prediction in the ICU. The XGBoost-DART model, with its outstanding performance, empowers clinicians to identify high-risk patients and initiate timely interventions to reduce mortality.<\/jats:p>","DOI":"10.1186\/s12911-026-03610-1","type":"journal-article","created":{"date-parts":[[2026,6,5]],"date-time":"2026-06-05T16:35:38Z","timestamp":1780677338000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Early prediction of sepsis in the ICU: a comparative analysis of multiple machine-learning algorithms using the MIMIC-III database"],"prefix":"10.1186","volume":"26","author":[{"given":"Xiuli","family":"Shao","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ruiling","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yaqi","family":"Lan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinyue","family":"Gao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"De-Zong","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rui","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianpei","family":"Niu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,6,5]]},"reference":[{"key":"3610_CR1","doi-asserted-by":"publisher","unstructured":"Merino, et al. 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