Methodological Review of Classification Trees for Risk Stratification: An Application Example in the Obesity Paradox.
Background: Classification trees (CTs) are widely used machine learning algorithms with growing applications in clinical research, especially for risk stratification. Their ability to generate interpretable decision rules makes them attractive to healthcare professionals. This review provides an acc...
| Publicado en: | Nutrients Vol. 17; no. 11; pp. 1903 - 1920 |
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| Autores principales: | , , , , , , , , , , |
| Formato: | case study review tables/charts Journal Article |
| Publicado: |
MDPI
Jun2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=185867187&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 185867187 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 20726643 B0TT jtl: Nutrients issn: 20726643 maglogo: N pubinfo: dt: Jun2025 vid: 17 iid: 11 pid: 97109 pub: MDPI artinfo: ui: 185867187 185867187 185867187 10.3390/nu17111903 185867187 ppf: 1903 ppct: 17 formats: tig: atl: Methodological Review of Classification Trees for Risk Stratification: An Application Example in the Obesity Paradox. aug: au: Trujillano, Javier Serviá, Luis Badia, Mariona Serrano, José C. E. Bordejé-Laguna, María Luisa Lorencio, Carol Vaquerizo, Clara Flordelis-Lasierra, José Luis Martínez de Lagrán, Itziar Portugal-Rodríguez, Esther López-Delgado, Juan Carlos affil: IRBLLeida (Institut de Recerca Biomèdica de Lleida Fundació Dr. Pifarré), Av. Alcalde Rovira Roure, 80, 25198 Lleida, Spain sug: subj: Risk Assessment Methods Machine Learning Algorithms Classification Obesity Paradox Intensive Care Units Critically Ill Patients Nutrition Regression Chi Square Test Logistic Regression Obesity Classification Hospital Mortality Risk Factors ROC Curve Validity ab: Background: Classification trees (CTs) are widely used machine learning algorithms with growing applications in clinical research, especially for risk stratification. Their ability to generate interpretable decision rules makes them attractive to healthcare professionals. This review provides an accessible yet rigorous overview of CT methodology for clinicians, highlighting their utility through a case study addressing the "obesity paradox" in critically ill patients. Methods: We describe key methodological aspects of CTs, including model development, pruning, validation, and classification types (simple, ensemble, and hybrid). Using data from the ENPIC (Evaluation of Practical Nutrition Practices in the Critical Care Patient) study, which assessed artificial nutrition in ICU (intensive care unit) patients, we applied various CT approaches—CART (classification and regression trees), CHAID (chi-square automatic interaction detection), and XGBoost (extreme gradient boosting)—and compared them with logistic regression. SHAP (SHapley Additive exPlanation) values were used to interpret ensemble models. Results: CTs allowed for identification of optimal cut-off points in continuous variables and revealed complex, non-linear interactions among predictors. Although the obesity paradox was not confirmed in the full cohort, CTs uncovered a specific subgroup in which obesity was associated with reduced mortality. The ensemble model (XGBoost) achieved the best predictive performance (highest area under the ROC curve), though at the expense of interpretability. Conclusions: CTs are valuable tools in clinical epidemiology, complementing traditional models by uncovering hidden patterns and enhancing risk stratification. While ensemble models offer superior predictive accuracy, their complexity necessitates interpretability techniques such as SHAP. CT-based approaches can guide personalized medicine but require cautious interpretation and external validation. pubtype: Academic Journal doctype: case study review tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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