Development and Evaluation of an Optimal Machine Learning Model for Predicting Nutritional Risk in Nasopharyngeal Carcinoma Patients: A Cross-Sectional Study.
Aim: To develop a predictive model for nutritional risk in patients with nasopharyngeal carcinoma (NPC) and identify clinically meaningful ranges for key risk factors to guide early intervention. Methods: This study enrolled 520 patients with nasopharyngeal carcinoma (NPC) who underwent radiotherapy...
| Publicado en: | Nutrition & Cancer Vol. 78; no. 2; pp. 169 - 181 |
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| Autores principales: | , , , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Taylor & Francis Ltd
2026
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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=190553135&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 190553135 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01635581 7MS jtl: Nutrition & Cancer issn: 01635581 maglogo: N pubinfo: dt: 2026 vid: 78 iid: 2 pid: 377 pub: Taylor & Francis Ltd place: Philadelphia, Pennsylvania artinfo: ui: 190553135 189542391 190553135 190553135 10.1080/01635581.2025.2591494 190553135 ppf: 169 ppct: 12 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Development and Evaluation of an Optimal Machine Learning Model for Predicting Nutritional Risk in Nasopharyngeal Carcinoma Patients: A Cross-Sectional Study. aug: au: Zhu, Benxiang Gao, Chang Chen, Peijuan Zhang, Lu Liu, Lian Zhang, Lili affil: School of Nursing, Southern Medical University, Guangzhou, China sug: subj: Nasopharyngeal Carcinoma Complications Malnutrition Risk Factors Risk Assessment Machine Learning Evaluation Prediction Models Evaluation Malnutrition Therapy Early Intervention Reference Values Human Cancer Patients Cross Sectional Studies Male Female Adult Middle Age Hospitals China Body Mass Index Alanine Aminotransferase Nasopharyngeal Carcinoma Radiotherapy Nasopharyngeal Carcinoma Pathology Tumor Burden Severity of Illness Smoking Record Review Scales Machine Learning Algorithms ROC Curve Data Analysis Software T-Tests Nonparametric Statistics Chi Square Test Descriptive Statistics Funding Source Adult: 19-44 years Middle Aged: 45-64 years Male Female ab: Aim: To develop a predictive model for nutritional risk in patients with nasopharyngeal carcinoma (NPC) and identify clinically meaningful ranges for key risk factors to guide early intervention. Methods: This study enrolled 520 patients with nasopharyngeal carcinoma (NPC) who underwent radiotherapy at Guangzhou Nanfang Hospital from 2021 to 2024. Thirty-two baseline variables were collected, including body measurements, lab tests, treatment details, and lifestyle factors. Seven machine learning models were developed. Key predictors were selected using LASSO regression, and their importance was assessed using SHAP values. Results: The XGBoost model performed best, with an AUC of 0.775 on the validation set. Four main predictors of nutritional risk were identified: body mass index (BMI), alanine transaminase (ALT), clinical stage, and smoking status. Patients with a BMI between 21.5 and 24.9 kg/m2 and ALT values in the higher range of normal had a lower risk of malnutrition. These findings provide more specific guidance than existing tools. Conclusion: This study highlights the added value of combining clinical data and machine learning to identify both key predictors and their optimal ranges for nutritional risk. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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