RESUMEN
Background: Hypertension is a major public health problem, and its resulting other cardiovascular diseases are the leading cause of death worldwide. In this study, we constructed a convenient and high-performance hypertension risk prediction model to assist in clinical diagnosis and explore other important influencing factors. Methods: We included 8,073 people from NHANES (2017-March 2020), using their 120 features to form the original dataset. After data pre-processing, we removed several redundant features through LASSO regression and correlation analysis. Thirteen commonly used machine learning methods were used to construct prediction models, and then, the methods with better performance were coupled with recursive feature elimination to determine the optimal feature subset. After data balancing through SMOTE, we integrated these better-performing learners to construct a fusion model based for predicting hypertension risk on stacking strategy. In addition, to explore the relationship between serum ferritin and the risk of hypertension, we performed a univariate analysis and divided it into four level groups (Q1 to Q4) by quartiles, with the lowest level group (Q1) as the reference, and performed multiple logistic regression analysis and trend analysis. Results: The optimal feature subsets were: age, BMI, waist, SBP, DBP, Cre, UACR, serum ferritin, HbA1C, and doctors recommend reducing salt intake. Compared to other machine learning models, the constructed fusion model showed better predictive performance with precision, accuracy, recall, F1 value and AUC of 0.871, 0.873, 0.871, 0.869 and 0.966, respectively. For the analysis of the relationship between serum ferritin and hypertension, after controlling for all co-variates, OR and 95% CI from Q2 to Q4, compared to Q1, were 1.396 (1.176-1.658), 1.499 (1.254-1.791), and 1.645 (1.360-1.989), respectively, with P < 0.01 and P for trend <0.001. Conclusion: The hypertension risk prediction model developed in this study is efficient in predicting hypertension with only 10 low-cost and easily accessible features, which is cost-effective in assisting clinical diagnosis. We also found a trend correlation between serum ferritin levels and the risk of hypertension.
RESUMEN
Panonychus ulmi Koch is one of the important pest insects of apple production in China. To clarify the spatiotemporal dynamics of P. ulmi on the apple tree crowns in an apple orchard of Liaoning, Northeast China, an investigation with random sampling was conducted on the pest mite number at each direction and each layer of the crowns in the whole growth season from May to November 2007. The spatial distribution pattern and time series dynamics of P. ulmi were analyzed by calculating the indices of aggregation and using the parameters of Iwao model. In the early and mid growth periods of apple tree, P. ulmi within whole crown fitted negative binomial distribution, presented an aggregated pattern, and its fundamental component was the group composed of several individuals that attracted each other. The aggregation intensity showed a negative fluctuation with population density, namely, high population density but low patchiness density, and low population density but high patchiness density, and there existed definite differences at different crown directions and layers, i. e., the patchiness density was the highest in south direction and the lowest in west direction, and was higher in mid and lower layers than in upper layer, and in inner layer than in outer layer.
