{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T00:53:12Z","timestamp":1787014392951,"version":"build-2736575974"},"reference-count":41,"publisher":"Wiley","issue":"5","license":[{"start":{"date-parts":[[2025,9,23]],"date-time":"2025-09-23T00:00:00Z","timestamp":1758585600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Int J of Nursing Practice"],"published-print":{"date-parts":[[2025,10]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Aim<\/jats:title>\n                    <jats:p>The aim of this study is to develop and compare machine learning algorithms for hypertension prediction in low\u2010income populations, with emphasis on model interpretability for nursing implementation in resource\u2010limited settings.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>This retrospective cross\u2010sectional study analysed data from seven iterations of NHANES (2005\u20132018) focusing on low\u2010income populations. After LASSO regression identified eight key predictors, eight machine learning models were developed and evaluated using ROC curves, calibration plots and decision curve analysis, with SHAP methodology applied for interpretation.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Among 12\u2009506 participants, 39.96% had hypertension. Logistic regression and neural networks both achieved the highest discriminative ability (AUC\u2009=\u20090.853). SHAP analysis identified age as the most influential predictor, followed by waist circumference and diabetes status. A clinical nomogram with three\u2010tier risk stratification (&lt;\u200930%, 30%\u201360% and &gt;\u200960%) was developed for nursing assessment.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Neural network models with SHAP interpretation achieved optimal hypertension prediction (AUC\u2009=\u20090.853) while maintaining clinical transparency essential for nursing practice. The resulting nurse\u2010accessible nomogram with a visual scoring system supports evidence\u2010based screening in low\u2010income populations, pending external validation in clinical settings.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/ijn.70060","type":"journal-article","created":{"date-parts":[[2025,9,23]],"date-time":"2025-09-23T07:06:37Z","timestamp":1758611197000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Developing Nurse\u2010Accessible Hypertension Prediction Tools for Low\u2010Income Populations: A Comparative Analysis of Machine Learning Algorithms With SHAP Interpretation"],"prefix":"10.1111","volume":"31","author":[{"given":"Chuan","family":"Huang","sequence":"first","affiliation":[{"name":"School of Nursing Chengdu University of Traditional Chinese Medicine  Chengdu China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiaojiao","family":"Xu","sequence":"additional","affiliation":[{"name":"Affiliated Hospital of Sichuan Nursing Vocational College Third People's Hospital of Sichuan Province  Chengdu China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hai","family":"Qiu","sequence":"additional","affiliation":[{"name":"People's Hospital of Deyang City  Deyang China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-2352-2489","authenticated-orcid":false,"given":"Yuchuan","family":"Yue","sequence":"additional","affiliation":[{"name":"Fourth People's Hospital of Chengdu  Chengdu China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2025,9,23]]},"reference":[{"key":"e_1_2_10_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/S2214\u2010109X(19)30077\u20104"},{"key":"e_1_2_10_3_1","unstructured":"Awan A. 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