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The research on evaluation of diabetes metabolic function based on Support Vector Machine

机译:基于支持向量机的糖尿病代谢功能评估研究

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For metabolic diseases, functional changes are often earlier than structural lesions, for example, diabetes. The paper aims to provide a survey using Support Vector Machine (SVM) to predict and assess metabolic functions of diabetes based on bio-heat transfer theory and infrared thermal imaging technology. Two metabolic characteristic values, metabolic function parameter and blood perfusion rate, are extracted from thermography data of cold water stimulation experiment as inputs of SVM to set up models by different kernel functions. For more than 2000 clinical data used in the paper, the prediction accuracy averaged 90%. The research provides a new attempt to evaluate diabetes metabolic function, hoping for contribution to early detection of diabetes.
机译:对于代谢性疾病,功能变化通常早于结构性病变,例如糖尿病。本文旨在通过基于生物热传递理论和红外热成像技术的支持向量机(SVM)来预测和评估糖尿病的代谢功能,从而提供一项调查。从冷水刺激实验的热成像数据中提取两个代谢特征值,即代谢功能参数和血液灌注率,作为支持向量机的输入,以不同的核函数建立模型。对于本文使用的2000多个临床数据,预测准确性平均为90%。该研究提供了评估糖尿病代谢功能的新尝试,希望为早期发现糖尿病做出贡献。

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