title: Artificial neural network based model for cardiovascular risk stratification in hypertension.
abstract: This study was to develop an objective method to stratify cardiovascular risk in hypertension. Stratification for cardiovascular risk is crucial in deciding treatment strategy for hypertension but has yielded undesirable results in clinic due to its low accuracy which is caused by physicians' subjective experience and the uncertainty of patients' statements. Our model proposed herein overcomes these disadvantages by applying artificial neural network based on a classic back propagation net. The model input is derived from the clinical investigation. The target output is the stratification level of total cardiovascular risk, which is learned from the guidelines of hypertension treatment. Study in 348 normotensive and hypertensive subjects showed that the results of model stratification are consistent with the standard stratification suggested by hypertension guidelines in 81.61% cases. The results confirm the accuracy of the model and demonstrate its ability in risk evaluation for hypertension.
authors.affiliation: Department of Biomedical Engineering, Zhejiang University (Yuquan Campus), Zheda Road 38, 310027, Hangzhou, China. gmning@zju.edu.cn
authors.forename: Gangmin
authors.initials: G
authors.lastname: Ning
authors.forename: Jie
authors.initials: J
authors.lastname: Su
authors.forename: Yingqi
authors.initials: Y
authors.lastname: Li
authors.forename: Xiaoying
authors.initials: X
authors.lastname: Wang
authors.forename: Chenghong
authors.initials: C
authors.lastname: Li
authors.forename: Weimin
authors.initials: W
authors.lastname: Yan
authors.forename: Xiaoxiang
authors.initials: X
authors.lastname: Zheng
mesh_terms: D000465:Algorithms; D001794:Blood Pressure / Q000502:physiology; D002318:Cardiovascular Diseases / Q000209:etiology*; D005260:Female; D006801:Humans; D006973:Hypertension / Q000150:complications; D008297:Male; D008955:Models, Cardiovascular; D016571:Neural Networks, Computer; D018570:Risk Assessment / Q000379:methods