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CLASSIFICATION OF METABOLIC SYNDROME PATIENTS USING IMPLEMENTED EXPERT SYSTEM

机译:采用实施专家系统代谢综合征患者的分类

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This paper presents the development of an Expert System for the classification of metabolic syndrome (MetS). Two-layer feedforward Artificial Neural Network (ANN) with sigmoid transfer function is used for MetS classification. In accordance with international guidelines NHBL/AHA, classification is performed based on following input parameters: waist circumference, blood pressure, glucose level, HDL cholesterol and triglycerides. Samples for training of developed Expert System are obtained from 1083 patients at hospitals in Bosnia and Herzegovina. Testing of developed system is performed with 300 samples, also acquired from patients in hospitals in B&H by medical professionals. Out of 300 samples, 155 samples were of MetS while the rest was of healthy subjects. Developed Expert System correctly classified 283 MetS samples, therefore the sensitivity of 96% is achieved and specificity is 92,7%.
机译:本文介绍了代谢综合征分类的专家系统的发展(METS)。具有Sigmoid传递函数的双层前馈人工神经网络(ANN)用于METS分类。根据国际指南NHBL / AHA,基于以下输入参数进行分类:腰围,血压,葡萄糖水平,HDL胆固醇和甘油三酯。开发专家系统的培训样本是从波斯尼亚和黑塞哥维那的1083名医院患者获得。开发系统的测试是用300个样本进行的,也从医疗专业人员的B&H中的医院中获取。在300个样品中,155个样品是遇到的,而其余的是健康的受试者。开发的专家系统正确分类283 Mets样品,因此实现了96%的灵敏度,并且特异性为92.7%。

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