Prediksi Diabetes Berdasarkan Pengukuran Mean Amplitude Glycemic Excursion (MAGE) Menggunakan Naïve Bayes

Lailis Syafa’ah, M. S. Ma'arif, Amrul Faruq
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Abstract

 The mean amplitude of glycemic excursions (MAGE) is an important indicator in the assessment of glycemic variability (GV) which is used as a reference for continuous blood glucose control. In this case, quantitative considerations in monitoring blood sugar in diabetes are very important for diagnosis and then proceed with clinical treatment. This study focuses more on strengthening the training and testing data processing system and reducing the independent variables that occur during the classification process. To support this purpose, this study uses Cross Validation as a training and testing data processing with the number of K-Fold is 10 and Naïve Bayes as a classification method. The resulting accuracy is 93% which is an increase from previous studies with an RMSE value (error value) of 0.267. It was concluded that patients in the pre-diabetic and diabetic groups tend to have more varied blood glucose values than patients from the normal class.
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基于Naïve Bayes平均振幅血糖偏移(MAGE)测量的糖尿病预测
血糖漂移平均振幅(MAGE)是评估血糖变异性(GV)的重要指标,可作为持续血糖控制的参考。在这种情况下,监测糖尿病患者血糖的定量考虑对于诊断和进行临床治疗非常重要。本研究更侧重于加强训练和测试数据处理系统,减少分类过程中出现的自变量。为了支持这一目的,本研究使用交叉验证作为训练和测试数据处理,K-Fold的次数为10,并使用Naïve贝叶斯作为分类方法。得到的准确度为93%,比以前的研究有所提高,RMSE值(误差值)为0.267。结论:糖尿病前期和糖尿病组患者的血糖值比正常组患者变化更大。
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