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.
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Neural network used in predicting cardiovascular risks 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@gmail.com1. Introduction No gold standard exists for assessing the risk of individual patients. Currenttechniques 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, andnot always immediately useable. The research is applying data mining methods tomake the clinical data more useable, meaningful and open to the use of neural andother classifier techniques. Risk assessment systems were designed and implemented to help the cliniciansin their decision for the patients particular cardiovascular uses. These systems sup-port the diagnosis based on medical data and knowledge domain. The quality ofmedical decision making will be improved by the support from these systems andclinical experiences. This research focuses on the popular system, which is usingbroadly in Britain medical decision support system, The Physiological OperativeSeverity 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 OrganizingMaps (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 systemof POSSUM and the advantage of Neural network. The comparisons are based onthe rate of mortality and morbidity of patients. The outcome set of unsupervisedlearning techniques is compared to the results of supervised ones.2. Data The given data is collected from Hull site and Dundee site in the hospitals ofBritain. They are allocated in many excel files. The original data included the infor-mation about cardiovascular patients. There are errors in row data such as duplicate40 Neural network used in predicting cardiovascular risksinformation, missing values, or are inconsistent. Original databases from Hull sitehas 98 attributes and 497 patients. This data is scored from the risk assessmentsystem of POSSUM, PPOSSUM. Data from the Dundee site is taken from patients’information at Dundee hospital. It includes 35 attributes and 341 patients. A part of the data is used from previous researcher [1]. This data was trans-formed to be appropriate values for using of neural networking of a number of soft-ware such as SNNS (Stuttgard Neural Network Simulator), and NeuroDimension.The research had a comparison of results between former research and the presentresearch. This is mentioned in [2]. This research focuses on the row data from Hulland Dundee site. Other files might be additional information for the research in thechoosing of the input, or output set for training processes. The data mining methods of preparing data for the process of neural networksare data cleaning with the duplicated ID information, missing value. The duplicatedID is treated as alternative person. The missing value is replaced by the defaultvalue of “Null”. For the patient who has numerous missing values in its content thispatient could potentially be ignored. Row data is transformed to an appropriate value for each neural networktechnique. The normalization of data is scaling data into the range of [0,1]. In the method of linear normalization the new values can be calculated by theformula: New value = (original value-minimum value)/(maximum value- minimumvalue). For example, the original value of IPSI% in Hull site data domain is 80, theminimization, and maximization value of this attribute is 30, and 99 respectively.So, the new value for IPSI% attribute will be (80-30)/(99-30) =0.725. Alternativelyother methods such as using mean and standard deviation, or decimal scaling ofeach attribute are used in order to scale the values in specific ranges of [0,1]. The data transformation is represented in attribute construction. The at-tributes with Boolean values can be transformed to the value of “0” or “1”. Moreover,the attributes with “symbol” values might be transformed to the following method:Dividing the original attribute to sub-attributes, which are equal the number of itsvalues. For example “Indication” attribute in Hull site has 4 values as A-F, ASx,CVA, TIA, so we divide by Indication_A-F, Indicattion_ASx, Indication_CVA,and Indication_TIA . Therefore, the sub-atrributes continue to be transformed tosmaller groups as possible. The last transformation usually is Boolean values of “0”or “1”. An example about the transformation of data can be seen in Table 1 below. Table 1. Example of one new sub-group value IDC (Indication) IDC_A-F IDC_ASx IDC_CVA IDC_TIA A-F 1 0 0 0 ASx 0 1 0 0 CVA 0 0 1 0 TIA 0 0 0 1 41 Nguyen Thi Thu Thuy3. Risk assessment systems The medical decision support system are populary reviewed in such as MYCIN;INTERNIST-QMR; the GLAsgow system for the diagnosis of DYSpepsia; AcutePhysiology and Chronic Health Evaluation [3] ...