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首页> 外文会议>2016 5th International Conference on Informatics, Electronics and Vision >On data classification for female subjects with unhealthy-level visceral fat
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On data classification for female subjects with unhealthy-level visceral fat

机译：关于女性内脏脂肪水平不健康的数据分类

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In this paper, a method of classifying the data of female subjects taking the specific health examination is presented, using self-organizing maps (SOM's). The proposed method focuses on female subjects fulfilling the standard specified by body mass index and abdominal circumference. It defines the following data classes: the class with subjects of which hemoglobin A1c (HbA1c) values and item values associated with a liver belong to unhealthy levels, that with subjects having HbA1c and triglyceride values included in unhealthy levels, and that with remaining subjects. It generates the data for normal SOM learning from values of twelve items such as HbA1c and glutamic-oxaloacetic. SOM learning is made to construct the map, and its neurons are labeled. The class of data to be checked depends on the label of a winner, when the data is presented. Experimental results establish that the proposed method achieves the reasonably favorable accuracy of consistency on data classification.
机译：本文提出了一种使用自组织映射（SOM）对参加特定健康检查的女性受试者的数据进行分类的方法。拟议的方法侧重于满足由体重指数和腹围指定的标准的女性受试者。它定义了以下数据类别：具有与肝脏相关的血红蛋白A1c（HbA1c）值和项目值属于不健康水平的受试者的类别，具有不健康水平中包含HbA1c和甘油三酸酯值的受试者的类别以及其余受试者的类别。它从HbA1c和谷氨酸-草酰乙酸这样的12个项目的值生成用于正常SOM学习的数据。进行SOM学习以构造图，并标记其神经元。显示数据时，要检查的数据类别取决于获奖者的标签。实验结果表明，该方法在数据分类上达到了相当令人满意的一致性精度。

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