tailieunhanh - Neural network used in predicting cardiovascular risks

The techniques of supervised ones are applied to the data domain in order to have a comparison between the evaluated system of POSSUM and the advantage of Neural network. The comparisons are based on the rate of mortality and morbidity of patients. The outcome set of unsupervised learning techniques is compared to the results of supervised ones. | JOURNAL OF SCIENCE OF HNUE FIT. 2011 Vol. 56 pp. 40-47 NEURAL NETWORK USED IN PREDICTING CARDIOVASCULAR RISKS Nguyen Thi Thu Thuy Informatics Department The Commercial University of Viet nam HoTungMau Rd CauGiay Hanoi Email nguyentthuthuy@ 1. Introduction No gold standard exists for assessing the risk of individual patients. Current techniques use a generic technique applied to the patient s cardiovascular record. This data itself is inconsistent over a history of patients at any one clinical site and not always immediately useable. The research is applying data mining methods to make the clinical data more useable meaningful and open to the use of neural and other classifier techniques. Risk assessment systems were designed and implemented to help the clinicians in their decision for the patients particular cardiovascular uses. These systems sup- port the diagnosis based on medical data and knowledge domain. The quality of medical decision making will be improved by the support from these systems and clinical experiences. This research focuses on the popular system which is using broadly in Britain medical decision support system The Physiological Operative Severity Score for enUmeration of Mortality and morbidity POSSUM . The research focuses on the using of both supervised learning and unsuper- vised techniques in the medical domain in particular to the cardiovascular domain. These techniques are Multi-Layer Perceptron MLP Radial Basic Function RBF and Support Vector Machine SVM for supervised learning and Self Organizing Maps SOM for unsupervised learning. The techniques of supervised ones are ap- plied to the data domain in order to have a comparison between the evaluated system of POSSUM and the advantage of Neural network. The comparisons are based on the rate of mortality and morbidity of patients. The outcome set of unsupervised learning techniques is compared to the results of supervised ones. 2. Data The given data is collected from Hull site .

• Quản trị mạng
• Neural network
• Predicting cardiovascular risks
• Multi Layer Perceptron
• Radial basic function
• Support vector machine