{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,9]],"date-time":"2026-10-09T16:24:36Z","timestamp":1791563076917,"version":"4.3.4"},"reference-count":32,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2025,9,27]],"date-time":"2025-09-27T00:00:00Z","timestamp":1758931200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["www.mdpi.com"],"crossmark-restriction":true},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Background\/Objectives: Severe acute pancreatitis (SAP) carries substantial morbidity and resource burden, and early risk stratification remains challenging with conventional scores that require serial observations. The aim of this study was to develop and compare supervised machine-learning (ML) pipelines\u2014integrating feature selection and SHAP-based explainability\u2014for early prediction of SAP at emergency department (ED) presentation. Methods: This retrospective, single-center cohort was conducted in a tertiary-care ED between 1 January 2022 and 1 January 2025. Adult patients with acute pancreatitis were identified from electronic records; SAP was classified per the Revised Atlanta criteria (persistent organ failure \u2265 48 h). Six feature-selection methods (univariate AUROC filter, RFE, mRMR, LASSO, elastic net, Boruta) were paired with six classifiers (kNN, elastic-net logistic regression, MARS, random forest, SVM-RBF, XGBoost) to yield 36 pipelines. Discrimination, calibration, and error metrics were estimated with bootstrapping; SHAP was used for model interpretability. Results: Of 743 patients (non-SAP 676; SAP 67), SAP prevalence was 9.0%. Compared with non-SAP, SAP patients more often had hypertension (38.8% vs. 27.1%) and malignancy (19.4% vs. 7.2%); they presented with lower GCS, higher heart and respiratory rates, lower systolic blood pressure, and more frequent peripancreatic fluid (31.3% vs. 16.9%) and pleural effusion (43.3% vs. 17.5%). Albumin was lower by 4.18 g\/L, with broader renal\u2013electrolyte and inflammatory derangements. Across the best-performing models, AUROC spanned 0.750\u20130.826; the top pipeline (RFE\u2013RF features + kNN) reached 0.826, while random-forest-based pipelines showed favorable calibration. SHAP confirmed clinically plausible contributions from routinely available variables. Conclusions: In this study, integrating feature selection with ML produced accurate and interpretable early prediction of SAP using data available at ED arrival. The approach highlights actionable predictors and may support earlier triage and resource allocation; external validation is warranted.<\/jats:p>","DOI":"10.3390\/diagnostics15192473","type":"journal-article","created":{"date-parts":[[2025,9,29]],"date-time":"2025-09-29T11:40:52Z","timestamp":1759146052000},"page":"2473","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Integrating Feature Selection, Machine Learning, and SHAP Explainability to Predict Severe Acute Pancreatitis"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9703-8344","authenticated-orcid":false,"given":"\u0130zzet","family":"Ustaalio\u011flu","sequence":"first","affiliation":[{"name":"Department of Emergency Medicine, G\u00f6nen State Hospital, 10900 Bal\u0131kesir, T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8324-3264","authenticated-orcid":false,"given":"Rohat","family":"Ak","sequence":"additional","affiliation":[{"name":"Department of Emergency Medicine, Health Sciences University, Kartal Dr. L\u00fctfi K\u0131rdar City Hospital, 34865 \u0130stanbul, T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,9,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"330","DOI":"10.1097\/MPA.0000000000001757","article-title":"The Short- and Long-Term Burden of Acute Pancreatitis in the United States: A Retrospective Cohort Study","volume":"50","author":"Pokras","year":"2021","journal-title":"Pancreas"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"726","DOI":"10.1016\/S0140-6736(20)31310-6","article-title":"Acute Pancreatitis","volume":"396","author":"Boxhoorn","year":"2020","journal-title":"Lancet"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Li, C.-L., Jiang, M., Pan, C.-Q., Li, J., and Xu, L.-G. 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