Construction Of Blood Pressure Prediction Model Based On Deep Learning And Traditional Chinese Medicine Face Diagnosi

Nowadays,cardiovascular disease patients are becoming more and more common as a result of unhealthy lifestyle habits like obesity,drinking,lacking of exercise,and unhealthy diet.Hypertension is a substantial risk factor for cardiovascular disease.In the early stages of hypertension,no distinct symptoms exist.It has become crucial to learn how to successfully monitor and avoid hypertension.Currently,upper arm type or wrist strap type electronic sphygmomanometers are the most widely used pressured blood pressure monitoring equipment.However,these instruments have limited use scenarios.Non-contact blood pressure monitoring is essential for the treatment,management,and prevention of hypertension and other cardiovascular problems because it can continually track the change in blood pressure over an extended period of time.It is beneficial to employ deep learning techniques in order to establish a blood pressure prediction model,increase the rate at which potential hypertension patients are recognized,and create a new concept and procedure for hypertension screening.This is due to advancements in information and signal processing technology,particularly the quick rise of artificial intelligence information processing technology.It is crucial to precisely identify and choose the representation information of human blood pressure when modeling blood pressure.Face diagnosis in traditional Chinese medicine(TCM),which has the advantages of causing no harm and can spot early illnesses in order to prevent disease,is an adjunct diagnosis approach to western medicine based on knowledge about the human face.Therefore,the application of deep learning information processing technology and face diagnosis theory to non-contact blood pressure measurement and hypertension prediction would be of considerable value.Face diagnosis in TCM,however,frequently uses "qualitative"and contentious diagnostic signs and findings.Therefore,a challenging issue in blood pressure prediction modeling and analysis is the achievement of objectification and quantitative analysis of face diagnosis.Contents and methods:First,the face reflex regions in TCM were chosen as the facial region of interest based on the Multi Scenario Sign Dataset(MSSD)for collecting blood pressure values,and the deep learning network was utilized to develop the prediction model of blood pressure values.In order to frame the five facial regions that correspond to the heart,liver,spleen,lung,and kidney of the face reflex regions,we used facial image detection technology in combination with transdermal optical imaging(TOI)technology.Our goal was to explore how different face reflex regions would affect the prediction model and evaluate their role in the prediction.Then,in order to forecast hypertension,we employed the Blood Pressure Dataset of Jidong Oilfield Employees.We divided the population into patients with hypertension and those without hypertension using the deep learning network.Prediction tests are carried out on clinical index data and face picture data.And a multimodal fusion network with feature collocation is presented to predict hypertension and enhance the discriminative accuracy of the disease.Results:1.To forecast blood pressure,a model is proposed in this study that combines the pulse wave signal concept,deep learning networks,and the theory of face diagnosis in TCM.Our very accurate,lightweight deep learning network can accurately monitor blood pressure and has some potential for use in real-world scenarios.2.In this study,a multimodal fusion hypertension prediction model was created,effectively fusing facial images and clinical indicators as two different types of modal data.The modal fusion improved the deep learning network model’s ability to discriminate between hypertensive patients and non-hypertensive people,and the accuracy of screening was higher than that of a single modality.The deep learning network model’s diagnostic ability is improved via multimodal fusion.3.In this study,a sizable sample of multimodal blood pressure data was compiled utilizing interdisciplinary techniques,comprising data on face images,clinical indexes,and blood pressure readings from patients with and people without hypertension.This comprehensive and precise multimodal blood pressure information database can provide standardized data to research related to intelligentized Chinese medicine.It can also be utilized to support blood pressure regression and classification investigations and promote the creation and application of intelligentized Chinese medicine.4.By combining the face reflex regions with the region of interest(ROI),the blood pressure prediction model was examined in accordance with the theory of face diagnosis,and it was discovered that the prediction performance was satisfactory.It was discovered that the right cheek(lung),left cheek(liver),and chin(kidney)regions may all provide a more significant contribution to the prediction model.5.To determine the color traits of hypertension,the color disparities between patients with hypertension and people without hypertension were examined using image processing technologies.The visualization method was used to highlight key facial features that are important for predicting hypertension.This can help to further recognize the link between TCM facial diagnosis and hypertensive diseases and support the execution of future pathomechanical analyses of hypertension in TCM.These findings help shed light on the scientific basis of TCM face diagnosis and the veracity of its usage in the detection and treatment of blood pressure issues,thereby offering more precise and scientific support.Conclusion:In this study,we built a deep learning multimodal fusion network-based hypertension disease prediction model as well as a high-precision and lightweight blood pressure values prediction model,with prediction accuracy of more than 85%and 90%respectively.Using image processing technology to investigate the connection between face diagnosis and hypertension is scientifically sound and applicability,according to experiments.Our model can forecast effectively and has great stability and research value thanks to the study.This initiative integrates statistical techniques,computer technology,and traditional Chinese medicine.This study offers the design concepts,experimental scenarios,and practical applications of the prediction model based on the principles of data collecting,experimental analysis,and model evaluation.Last but not least,a blood pressure prediction model that combines elements of face diagnosis is developed.This model offers methodological guidance and technical support for the study of blood pressure disorders.