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- Title
Artificial neural network based model for cardiovascular risk stratification in hypertension.
- Authors
Ning, Gangmin; Su, Jie; Li, Yingqi; Wang, Xiaoying; Li, Chenghong; Yan, Weimin; Zheng, Xiaoxiang
- 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.
- Subjects
ALGORITHMS; BIOLOGICAL models; BLOOD pressure; CARDIOVASCULAR diseases; COMPARATIVE studies; HYPERTENSION; RESEARCH methodology; MEDICAL cooperation; ARTIFICIAL neural networks; RESEARCH; RISK assessment; EVALUATION research; DISEASE complications
- Publication
Medical & Biological Engineering & Computing, 2006, Vol 44, Issue 1/2, p202
- ISSN
0140-0118
- Publication type
journal article
- DOI
10.1007/s11517-006-0028-2