As a common disease in the population, hypertension is an important risk factor for heart failure, coronary heart disease, aortic dissection, stroke and other cardiovascular and cerebrovascular diseases, which has a great impact on the health of the population. As the proportion of the number of patients increased year by year, the disease burden caused by hypertension also increased year by year. The interaction and multicollinearity among the risk factors that may affect the incidence of hypertension may lead to errors in the fitting of traditional models. The emerging research of machine algorithm can provide more possibilities for the analysis of disease prevention and control. BP neural network has great potential in the research of medical field, and the optimization of neural network can further improve its prediction and risk assessment performance, which has certain research significance.
In this study, a neural network prediction model of hypertension was established based on the monitoring data of chronic diseases and risk factors, and the POS algorithm was further used to complete the optimization of the neural network. Considering that too many neurons in the input layer will have higher requirements on the sample size, this study selected the independent variables preliminarily by referring to literatures and combining existing monitoring data, and took whether the patient had hypertension as the dependent variable to enter the neural network model. In order to prevent model overfitting and ensure test accuracy, the data set is divided into training set and test set in a ratio of 4:1. In addition, the establishment of neural network is flexible, and there is no uniform value of function and parameters in the process of establishment. Through comparison, it can be seen that the performance of BP neural network and BP neural network optimized by PSO is different during and after modeling. Therefore, after many times of training, the best prediction indexes in BP neural network and PSO-BP neural network were selected for horizontal comparison.
In the construction model, the iteration times and running time of BP neural network and PSO-BP neural network are significantly different. BP can achieve the best performance in a single iteration with four runs of about 10 seconds, while PSO-BP can achieve the best performance only when the iteration reaches the maximum number of times initially set, and the running time is about 500 seconds. However, in Zhang Yijun's study, the optimization algorithm will lead to shorter modeling time , which is different from this study. This may be because when the two algorithms are combined to achieve local and global optimality, more iterations and runtimes are needed to achieve the optimal performance of the model. In addition, the error of PSO-BP neural network has converged in the 10th iteration, and the optimal solution of this parameter is obtained. However, after the combination, it needs to iterate 5000 times to get the lowest root mean square error. It is comprehensively verified that PSO-BP neural network may have poor local search ability and need more iteration times.
In the comparison of the fitting ability and the performance of the training data set constructed by the model, the root mean square error of BP and PSO-BP is 0.34 and 0.09 respectively. When evaluating the performance ability of network fitting, the total coefficient of determination of BP neural network is 0.16, and the coefficient of determination of PSO-BP is 0.29, which is closer to 1 than that of BP neural network, indicating that the data set of PSO-BP model is more correlated with the reality. This result is consistent with Liu Xin's research results in strain prediction of wind turbine blades by using PSO-BP neural network, that is, PSO-BP neural network has a small error but higher fitting ability . Through the comparison of root mean square error and determination coefficient, it can be concluded that PSO-BP nonlinear fitting ability is better. In addition, the gradient descent curve of PSO-BP neural network is more stable than that of BP neural network, which also proves that PSO-BP neural network has better stability.
In addition, in the prediction performance comparison, the data of each model does not change much in multiple runs. The optimal operating results of each model were selected for horizontal comparison, and the predictive ability of BP neural network, PSO-BP neural network and Logistic regression was observed. Combined with the current data, this study found that compared with Logistic, BP neural network and PSO-BP neural network were significantly improved in accuracy, specificity and AUC. In view of accuracy and specificity can measure the accuracy of prediction, and AUC, as a performance index to measure the advantages and disadvantages of the learner, can be used to judge the advantages and disadvantages of the prediction model. The above results show that the neural network has better prediction accuracy than the traditional model. Through the comparison of neural network algorithm, it can be found that the accuracy, sensitivity and AUC of PSO-BP neural network prediction model are improved after POS algorithm optimization. Overall, PSO-BP neural network has the best performance in prediction and diagnosis. This result is consistent with the results of prediction performance comparison between neural network and traditional model that are mostly discussed at present [10, 18], indicating that PSO-BP neural network can also be well applied in the prediction of hypertension risk. However, it is undeniable that, unlike other studies, the sensitivity of the neural network in this study has been reduced, indicating that the diagnostic ability of the neural network constructed in this study has been reduced when predicting the risk of local hypertension. In conclusion, the neural network model has better adaptability and fitting effect for diseases such as hypertension, where there are many pathogenic factors and there may be interactions among various factors. Although the PSO-BP modeling time has been extended, the error is smaller, the correlation degree is higher, the nonlinear fitting ability is better, and the prediction performance is better. It indicates that the performance of BP neural network has been improved after optimization, and PSO-BP can be better applied to the study of hypertension risk.
In addition, since MIV algorithm is often used in engineering , meteorology, circuit technology and other aspects to screen risk factors, it can complete the screening of influencing factors and has a good identification performance. In this study, MIV algorithm is further used to complete the screening of risk factors for hypertension diseases in Guangdong region in the neural network model. The greater the absolute value of MIV, the greater the influence of the influencing factors on hypertension; The greater the weight of MIV, the greater the influence of this factor on rank. However, there is no unified standard for how much the MIV value can be regarded as the influencing factor. Through literature review, combined with professional knowledge and comparative prior knowledge, the absolute value > 0.002 of the self-determined factor MIV in this study can be considered as the influencing factor of hypertension in this area, and the influencing factors are ranked according to the MIV weight. In the prediction of BP neural network, it can be obtained through the screening of MIV algorithm that the risk factors of the disease in Guangdong area from heavy to light can be low-density lipoprotein cholesterol, cholesterol, sleep duration, daily oil intake, daily salt intake, smoking, age, BMI index, heart rate, high-density lipoprotein cholesterol, drinking, hemoglobin. The PSO-BP neural network analyzes the risk factors of the disease in the region, and the risk factors that affect high blood pressure are in order of cholesterol, sleep duration, low-density lipoprotein cholesterol, daily oil intake, triglycerides, Daily salt intake, gender (categorical variable), alcohol consumption, age, high-density lipoprotein cholesterol, hemoglobin, BMI index, alcohol consumption. Through comparison, it can be concluded that the selected risk factors obtained by MIV analysis under BP neural network and PSO-BP neural network model are different, and the weight of risk factors will also be changed, indicating that the algorithm optimization will produce differences in the establishment of prediction model.
It is worth noting that in comparison with the Logistic regression model for screening risk factors, the neural network MIV algorithm screening factors are different. The BP neural network screens cholesterol, sleep duration, daily oil intake, daily salt intake, and Drinking and other factors, while the more risk factors in the PSO-BP neural network are cholesterol, sleep duration, daily oil intake, triglycerides, daily salt intake, and alcohol consumption. However, the neural network lacks education as a risk factor, which shows that when the neural network-based MIV algorithm is used to screen risk factors in the analysis of hypertension, there will also be differences in the results of screening factors. Finally, after a comprehensive comparison between the fitting performance and the prediction performance, the PSO-BP neural network has the best performance. Based on this research, the PSO-BP neural network is finally selected as the prediction model for hypertension in Guangdong region.
Studies have shown that the risk factors for cardiovascular development are different in the elderly and the young . Among the subjects in this study, age was a positively correlated risk factor, and the risk of disease increased with age. In addition, gender is also a risk factor, and the gender difference is significant. In this study, the incidence of female is lower than that of male. In Tao Hong's study of hypertension population, the incidence of hypertension in women is also lower than that in men, which may be related to the fact that women pay more attention to hypertension and have better compliance than men, and are more able to adhere to a good lifestyle than men . Therefore, the prevention and control work in this region can be considered to take targeted measures for different groups of people to improve the effectiveness of publicity and the degree of concern.
Although the specific mechanism is unclear, more and more studies have shown that people with abnormal lipid indexes have a higher prevalence of hypertension than normal people . The results of model screening in this study indicate that cholesterol is the primary positively correlated risk factor in this area, and the risk of hypertension will increase with the increase of cholesterol content. In addition, the risk of hypertension increased with increases in LDL cholesterol and triglycerides, and increased with decreases in HDL cholesterol and hemoglobin. This screening is consistent with multiple studies showing that cholesterol, LDL cholesterol, and triglycerides are associated with an increased risk of hypertension, while HDL cholesterol has cardiovascular benefits [24, 25]. Reduced hemoglobin usually indicates a relatively poor health status of the human body, which may lead to hypertension . Studies have shown that the incidence of dyslipidemia can be effectively reduced by controlling body weight, blood sugar, blood pressure and consumption of meat products . To sum up, lifestyle change and drug intervention can be considered to reduce the occurrence of dyslipidemia, so as to reduce the incidence of hypertension.
In terms of life risk factors, the sleep duration in this study model is the primary risk factor with negative correlation, indicating that the decrease of sleep duration will lead to the increase of the risk of hypertension. In recent years, more researchers have paid attention to the relationship between this risk factor and hypertension . Studies believe that adequate sleep time is conducive to the control of blood pressure , and this study further supports this view. Based on this study, daily oil intake, daily salt intake, smoking, BMI and alcohol consumption were positively related factors, and the risk of hypertension increased with their increase. These risk factors overlap with those proposed in China's hypertension prevention and control guidelines .
In conclusion, based on the PSO-BP model, the risk factors of hypertension in Guangdong were analyzed from the perspective of social prevention and control. On the one hand, health education can be carried out in this region to guide people to choose the right lifestyle to prevent hypertension, including the promotion of healthy diet such as low-salt and low-oil diet , and the promotion of people to reduce the proportion of high-cholesterol food in their daily diet. In addition, promote the industry toward the development conducive to the health of the population, such as the establishment of food counters, set the content of oil and salt in finished or semi-finished products, etc. Encourage the development of corresponding health industry, such as fitness, yoga, etc. On the other hand, from the perspective of personal prevention, first of all, we should reduce staying up all night and develop good work and rest habits; actively develop healthy eating strategies to control salt and oil intake in daily life. At the same time, control smoking or stay away from smoking environments, limit alcohol consumption, and maintain or control body mass index within a healthy range.