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

Lailis Syafa’ah(1), M Syaiful Ma’arif(2), Amrul Faruq(3*)

(1) Jurusan Teknik Elektro, Fakultas Teknik Universitas Muhammadiyah Malang
(2) Jurusan Teknik Elektro, Fakultas Teknik Universitas Muhammadiyah Malang
(3) Jurusan Teknik Elektro, Fakultas Teknik Universitas Muhammadiyah Malang
(*) Corresponding Author


 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.


Diabetes Mellitus; Glycemic Variability; Cross Validation; Naïve Bayes

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[1] D. O. F. Diabetes, “Diagnosis and classification of diabetes mellitus,” Diabetes Care, vol. 33, no. SUPPL. 1, 2010.

[2] T. A. Peyser, A. K. Balo, B. A. Buckingham, I. B. Hirsch, and A. Garcia, “Glycemic Variability Percentage: A Novel Method for Assessing Glycemic Variability from Continuous Glucose Monitor Data,” Diabetes Technol. Ther., vol. 20, no. 1, pp. 6–16, 2018, [Online]. Available: [Accessed: 16-Aug-2020]

[3] X. Yu et al., “Calculating the Mean Amplitude of Glycemic Excursions from Continuous Glucose Data Using an Open-Code Programmable Algorithm Based on the Integer Nonlinear Method,” Comput. Math. Methods Med., vol. 2018, 2018, [Online]. Available: [Accessed: 16-Aug-2020]

[4] L. Syafa ’ah, A. Tjokroprawiro, M. Rasad Indra, D. Sargowo, and 5 Muladi, “Expert System for Blood Glucose Fluctuations Measurement Based on MAGE (Mean Amplitude of Glycemic Excursion) and HbA1c On Diabetic Using K-NN (Nearest Neighbor),” J. Basic. Appl. Sci. Res, vol. 4, no. 12, pp. 135–141, 2014 [Online]. Available: [Accessed: 10-Jul-2020]

[5] P. A. Baghurst, “Calculating the mean amplitude of glycemic excursion from continuous glucose monitoring data: An automated algorithm,” Diabetes Technol. Ther., vol. 13, no. 3, pp. 296–302, 2011, [Online]. Available: [Accessed: 13-Aug-2020]

[6] G. Fritzsche, K. D. Kohnert, P. Heinke, L. Vogt, and E. Salzsieder, “The use of a computer program to calculate the mean amplitude of glycemic excursions,” Diabetes Technol. Ther., vol. 13, no. 3, pp. 319–325, 2011, [Online]. Available: [Accessed: 16-Aug-2020]

[7] J. Ker, L. Wang, J. Rao, and T. Lim, “Deep Learning Applications in Medical Image Analysis,” IEEE Access, vol. 6, no. May 2018, pp. 9375–9379, 2017, [Online]. Available: [Accessed: 17-Aug-2020]

[8] I. N. T. Wirawan and I. Eksistyanto, “Penerapan Naive Bayes Pada Intrusion Detection System Dengan Diskritisasi Variabel,” JUTI J. Ilm. Teknol. Inf., vol. 13, no. 2, p. 182, 2015, [Online]. Available: [Accessed: 16-Aug-2020]

[9] D. Xhemali, C. J. Hinde, and R. G. Stone, “Naive Bayes vs. Decision Trees vs. Neural Networks in the Classification of Training Web Pages,” Int. J. Comput. Sci., vol. 4, no. 1, pp. 16–23, 2009. [Online]. Available: [Accessed: 16-Aug-2020]

[10] L. Syafa’ah, M. H. Purnomo, and S. Basuki, “Discrete mean amplitude of glycemic excursion (MAGE) measurement on diabetics with spline interpolation method,” Int. J. Electr. Eng. Informatics, vol. 10, no. 2, pp. 259–270, 2018, [Online]. Available: [Accessed: 16-Aug-2020]

[11] L. Syafaah, S. Basuki, F. Dwi, and S. Sumadi, “Study on Diabetes Prediction Based on Discrete and Continuous Mean Amplitude of Glycemic Excursions ( MAGE ) using Machine Learning Methods,” vol. 8, no. 4, 2019, [Online]. Available: [Accessed: 21-Aug-2020]

[12] J. H. DeVries, “Glucose variability: Where it is important and how to measure it,” Diabetes, vol. 62, no. 5, pp. 1405–1408, 2013, [Online]. Available: [Accessed: 16-Aug-2020]

[13] M. Heller, P. Edelstein, and M. Mayer, “Membrane-bound enzymes. III. Protease activity in leucocytes in relation to erythrocyte membranes,” BBA - Biomembr., vol. 413, no. 3, pp. 472–482, 1975, [Online]. Available: [Accessed: 16-Aug-2020]

[14] F. Cavalot, “Do data in the literature indicate that glycaemic variability is a clinical problem? Glycaemic variability and vascular complications of diabetes,” Diabetes, Obes. Metab., vol. 15, no. S2, pp. 3–8, 2013, [Online]. Available: [Accessed: 1-Sep-2020]

[15] A. A. Kazi and L. Blonde, Classification of diabetes mellitus, vol. 21, no. 1. 2001.

[16] S. Suh and J. H. Kim, “Glycemic variability: How do we measure it and why is it important?,” Diabetes Metab. J., vol. 39, no. 4, pp. 273–282, 2015, [Online]. Available: [Accessed: 16-Aug-2020]

[17] J. Wadén, C. Forsblom, L. M. Thorn, D. Gordin, M. Saraheimo, and P. H. Groop, “A1C variability predicts incident cardiovascular events, microalbuminuria, and overt diabetic nephropathy in patients with type 1 diabetes,” Diabetes, vol. 58, no. 11, pp. 2649–2655, 2009, [Online]. Available: [Accessed: 16-Aug-2020]

[18] F. J. Service, G. D. Molnar, J. W. Rosevear, E. Ackerman, L. C. Gatewood, and W. F. Taylor, “Mean amplitude of glycemic excursions, a measure of diabetic instability.,” Diabetes, vol. 19, no. 9, pp. 644–655, 1970, [Online]. Available: [Accessed: 16-Aug-2020]

[19] D. V. Griffiths and I. M. Smith, “Numerical methods for engineers, second edition,” Numer. Methods Eng. Second Ed., pp. 1–475, 2006.

[20] M. Irfan, W. Uriawan, O. T. Kurahman, M. A. Ramdhani, and I. A. Dahlia, “Comparison of Naive Bayes and K-Nearest Neighbor methods to predict divorce issues,” IOP Conf. Ser. Mater. Sci. Eng., vol. 434, no. 1, 2018, [Online]. Available: [Accessed: 16-Aug-2020]

[21] Bustami, “Penerapan Algoritma Naive Bayes untuk Mengklasifikasi Data Nasabah,” TECHSI J. Penelit. Tek. Inform., vol. 4, pp. 127–146, 2010. [Online]. Available: [Accessed: 16-Aug-2020]

[22] K. Chai, H. T. Hn, and H. L. Cheiu, “Naive-Bayes Classification Algorithm,” Proc. 25th Annu. Int. ACM SIGIR Conf. Res. Dev. Inf. Retr., pp. 97–104, 2002,

[23] H. Rosdianto and M. Toifur, “Implementasi Teori Distribusi Probabilitas Gaussian Pada Kualitas Rangkaian Penyearah Gelombang Penuh,” Spektra J. Fis. dan Apl., vol. 2, no. 1, pp. 83–90, 2017, [Online]. Available: [Accessed: 16-Aug-2020]

[24] C. R. Wilson Van Voorhis and B. L. Morgan, “Understanding Power and Rules of Thumb for Determining Sample Sizes,” Tutor. Quant. Methods Psychol., vol. 3, no. 2, pp. 43–50, 2007, [Online]. Available: [Accessed: 16-Aug-2020]


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