BP Neural Network Model for Late-Night Effects Prediction in Postgraduates' Hypertension

Graduate students exhibit a higher propensity for nocturnal hypertension episodes compared to the general working population. This study investigated the correlation between blood pressure and various physiological parameters, developing a continuous blood pressure prediction model based on highly correlated characteristics. Using a convenience sampling method involving physical examinations and structured questionnaires, 119 master's and doctoral students were selected. A BP neural network prediction model was constructed to analyze the impact of late-night academic work and dietary habits on blood pressure, identifying key influencing factors. Analysis demonstrated the model's high accuracy in blood pressure prediction, facilitating personalized health behavior recommendations. Evaluation metrics such as mean absolute error (MAE), standard deviation of error (SDE), root mean square error (RMSE), and R ² were employed, achieving MAE = 3.56, SDE = 5.16, RMSE = 8.09, and R ²  = 0.99866 for systolic blood pressure (SBP), and MAE = 3.34, SDE = 4.46, RMSE = 7.26, and R ²  = 0.99284 for diastolic blood pressure (DBP) across test sets. These results met the standards set by the Association for the Advancement of Medical Instrumentation (AAMI). Factor weight analysis identified sleep duration (19.39%) and body weight as key hypertension drivers, followed by exercise duration, dietary habits, and emotional regulation. Prioritizing these factors is critical for developing targeted interventions. Systematic analysis of health data in research cohorts, particularly quantifying their pathogenic contributions to hypertension pathogenesis, advances predictive modeling and precision treatment frameworks.