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首页> 外文会议>2012 IEEE International Conference on Systems, Man, and Cybernetics. >Study of Type 2 diabetes risk factors using neural network for Thai people and tuning neural network parameters
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Study of Type 2 diabetes risk factors using neural network for Thai people and tuning neural network parameters

机译：使用面向泰国人的神经网络和调整神经网络参数研究2型糖尿病风险因素

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摘要

Risk factors for Type 2 diabetes is very important for developing diabetes prediction tools instead of blood testing. Recently, many researches have studied risk factors of diabetes in order to apply them to be a tool for diabetes prediction by using Logistic Regression (LR), Radial Basis and Back-propagation Neural Network (BNN). However, the accuracy is not higher. This paper presents new factors that are smoking and alcohol consumption to improve accuracy in diabetes prediction. Some traditional factors i.e., body mass index (BMI), blood pressure (BP) and waist circumference (WC) and Family History (FMH) are also proposed to extent by adjusting and additional range. The proposed diabetes prediction method is based on BNN. Approximately 2,000 cases of Thai people at BMC hospital, Thailand during 2010 to 2012 are used to train the BNN. From experiment results, each proposed factors i.e., FMH, Alcohol consumption factor, Smoking Factors and WC gives a value of accuracy that is higher than baseline as 83.35%, 83.5%, 83.6% and 83.65%, respectively. After that, this paper focuses on tuning neural network parameter, which is divided into 3 main steps: number of hidden nodes, sequence of integrating the proposed factors, and other parameter i.e., learning rate, and Iteration. Finally, the proposed factors and tuning BNN parameters introduce a high accuracy compared with the baseline up to 1.2%.
机译：2型糖尿病的危险因素对于开发糖尿病预测工具而不是血液测试非常重要。近来，许多研究已经研究了糖尿病的危险因素，以便通过使用Logistic回归（LR），径向基和反向传播神经网络（BNN）将它们用作糖尿病的预测工具。但是，精度不高。本文介绍了吸烟和饮酒的新因素，以提高糖尿病预测的准确性。还提出了一些传统因素，例如体重指数（BMI），血压（BP）和腰围（WC）和家族史（FMH），以通过调整和增加范围来扩展。所提出的糖尿病预测方法是基于BNN的。在2010年至2012年期间，泰国BMC医院大约有2,000例泰国人用于训练BNN。从实验结果来看，建议的每个因素（即FMH，酒精消耗因子，吸烟因子和WC）得出的准确度值均高于基线，分别为83.35％，83.5％，83.6％和83.65％。之后，本文着重于神经网络参数的调整，该过程分为三个主要步骤：隐藏节点数，建议因子的集成顺序以及其他参数（即学习率和迭代）。最后，与基线相比，所提出的因素和调整BNN参数的准确性高达1.2％。

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