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الرئيسية > Clinical Decision Support Syst...>الوصف

Clinical Decision Support System: Risk Level Prediction Of Heart Disease Using Weighted Fuzzy Rules

المصدر: مجلة جامعة الملك سعود - علوم الحاسب والمعلومات
الناشر: جامعة الملك سعود
المؤلف الرئيسي: Anooj, P. K. (Author)
المجلد/العدد: مج24, ع1
محكمة: نعم
الدولة: السعودية
التاريخ الميلادي: 2012
الصفحات: 27 - 40
DOI: 10.33948/0584-024-001-004
ISSN: 1319-1578
رقم MD: 972031
نوع المحتوى: بحوث ومقالات
اللغة: الإنجليزية
قواعد المعلومات: science
مواضيع:
برامج الحاسوب | الشبكات العصبية | التشخيص الطبي | تشخيص الأمراض | أمراض القلب
كلمات المؤلف المفتاحية:
Clinical Decision Support System (CDSS) | Heart Disease | Fuzzy Logic | Weighted Fuzzy Rules | Attribute Selection | Risk Prediction | UCI Repository | Accuracy | Sensitivity And Specificity
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المستخلص: As people have interests in their health recently, development of medical domain application has been one of the most active research areas. One example of the medical domain application is the detection system for heart disease based on computer-aided diagnosis methods, where the data are obtained from some other sources and are evaluated based on computer based applications. Earlier, the use of computer was to build a knowledge based clinical decision support system which uses knowledge from medical experts and transfers this knowledge into computer algorithms manually. This process is time consuming and really depends on medical experts’ opinions which may be subjective. To handle this problem, machine learning techniques have been developed to gain knowledge automatically from examples or raw data. Here, a weighted fuzzy rule based clinical decision support system (CDSS) is presented for the diagnosis of heart disease, automatically obtaining knowledge from the patient’s clinical data. The proposed clinical decision support system for the risk prediction of heart patients consists of two phases: (1) automated approach for the generation of weighted fuzzy rules and (2) developing a fuzzy rule-based decision support system. In the first phase, we have used the mining technique, attribute selection and attribute weightage method to obtain the weighted fuzzy rules. Then, the fuzzy system is constructed in accordance with the weighted fuzzy rules and chosen attributes. Finally, the experimentation is carried out on the proposed system using the datasets obtained from the UCI repository and the performance of the system is compared with the neural network-based system utilizing accuracy, sensitivity and specificity.
ISSN: 1319-1578

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