Developing Nurse‐Accessible Hypertension Prediction Tools for Low‐Income Populations: A Comparative Analysis of Machine Learning Algorithms With SHAP Interpretation

Neural network models with SHAP interpretation achieved optimal hypertension prediction (AUC = 0.853) while maintaining clinical transparency essential for nursing practice. The resulting nurse-accessible nomogram with a visual scoring system supports evidence-based screening in low-income populations, pending external validation in clinical settings.