Comparison Data Mining Techniques To Prediction Diabetes Mellitus
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Published:
Sep 30, 2019
Keywords:
data mining, comparison, predict, diabetes mellitus
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Abstract
Diabetes is one of the chronic diseases caused by excess sugar in the blood. Various methods of automated algorithms in various to anticipate and diagnose diabetes. One approach to data mining method can help diagnose the patient's disease. In the presence of predictions can save human life and begin prevention before the disease attacks the patient. Choosing a legitimate classification clearly expands the truth and accuracy of the system as levels continue to increase. Most diabetics know little about the risk factors they face before the diagnosis. This method uses developing five predictive models using 9 input variables and one output variable from the dataset information. The purpose of this study was to compare performance analysis of Naive Bayes, Decision Tree, SVM, K-NN and ANN models to predict diabetes millitus
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SungeA. S. (2019). Comparison Data Mining Techniques To Prediction Diabetes Mellitus. Journal of Sustainable Engineering: Proceedings Series, 1(2), 225-230. https://doi.org/10.35793/joseps.v1i2.31
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Copyright (c) 2019 Author(s)
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K. Suhre et al., “Metabolic footprint of diabetes: A multiplatform metabolomics study in an epidemiological setting,” PLoS One, vol. 5, no. 11, 2010.
Sunge, Aswan S, “Optimasi Algoritma C4.5 Dalam Prediksi Web Phishing Menggunakan Seleksi Fitur Genetic Algoritma,” Paradigma, vol. 10, no. 2, pp. 27–32, 2018.
J. Steffi and D. R. B. 1M. Phil Student, “Predicting Diabetes Mellitus using Data Mining Techniques Comparative analysis of Data Mining Classification Algorithms,” Int. J. Eng. Dev. Res., vol. 6, no. 2, pp. 460–467, 2018.
S. R. P. Shetty and S. Joshi, “A Tool for Diabetes Prediction and Monitoring Using Data Mining Technique,” Int. J. Inf. Technol. Comput. Sci., vol. 8, no. 11, pp. 26–32, 2016.
R. S. S.Yuvarani, “Analysis of Decision Tree Models for Diabetes,” Int. Res. J. Eng. Technol., vol. 3, no. 11, pp. 680–684, 2016.
M. Layth, Z. Alkaragole, and A. Sefer Kurnaz, “Comparison of Data Mining Techniques for Predicting Diabetes or Prediabetes by Risk Factors,” Int. J. Comput. Sci. Mob. Comput., vol. 8, no. 3, pp. 61–71, 2019.
M. Kadhm, I. Ghindawi, D. M.-I. J. Of, and U. 2018, “An Accurate Diabetes Prediction System Based on K-means Clustering and Proposed Classification Approach,” Int. J. Appl. Eng. Res., vol. 13, no. 6, pp. 4038–4041, 2018.
H. Esmaily, M. Tayefi, H. Doosti, M. Ghayour-Mobarhan, H. Nezami, and A. Amirabadizadeh, “A Comparison between Decision Tree and Random Forest in Determining the Risk Factors Associated with Type 2 Diabetes.,” J. Res. Health Sci., vol. 18, no. 2, p. e00412, 2018.
S. Benbelkacem and B. Atmani, “Random Forests for Diabetes Diagnosis,” 2019 Int. Conf. Comput. Inf. Sci., pp. 1–4, 2019.
V. VijayanV and A. Ravikumar, “Study of Data Mining Algorithms for Prediction and Diagnosis of Diabetes Mellitus,” Int. J. Comput. Appl., vol. 95, no. 17, pp. 12–16, 2014.
C. Engineering, “Diabetes Prediction using Linear Regression , Decision Tree & Least Square,” Int. J. Innov. Res. Comput. Commun. Eng., vol. 6, no. 4, pp. 3756–3763, 2018.
D. Sisodia and D. S. Sisodia, “Prediction of Diabetes using Classification Algorithms,” Procedia Comput. Sci., vol. 132, no. Iccids, pp. 1578–1585, 2018.
M. Renuka Devi and J. Maria Shyla, “Analysis of various data mining techniques to predict diabetes mellitus,” Int. J. Appl. Eng. Res., vol. 11, no. 1, pp. 727–730, 2016.
S. Bano, M. Naeem, and A. Khan, “A Framework to Improve Diabetes Prediction using k-NN and SVM,” Int. J. Comput. Sci. Inf. Secur. (IJCSIS), vol. 14, no. 11, pp. 450–460, 2016.
K. Saravananathan and T. Velmurugan, “Impact of Classification Algorithms in Diabetes Data : A Survey,” 3rd Int. Conf. Small Mediu. Bus. 2016, pp. 271–275, 2016.
T. Marnoto, “Drying of Rosella (Hibiscus sabdariffa) Flower Petals using Solar Dryer with Double Glass Cover Collector,” Int. J. Sci. Eng., vol. 7, no. 2, pp. 155–160, 2014.
A. Iyer, S. Jeyalatha, and R. Sumbaly, “Diagnosis of Diabetes Using Classification Mining Techniques,” Int. J. Data Min. Knowl. Manag. Process, vol. 5, no. 1, pp. 1–14, 2015.
J. P. Kandhasamy and S. Balamurali, “Performance analysis of classifier models to predict diabetes mellitus,” Procedia Comput. Sci., vol. 47, no. C, pp. 45–51, 2014.
D. Vigneswari, N. K. Kumar, V. G. Raj, A. Gugan, and S. R. Vikash, “Machine Learning Tree Classifiers in Predicting Diabetes Mellitus,” 2019 5th Int. Conf. Adv. Comput. Commun. Syst., pp. 84–87, 2019.
A. S. Sunge and W. D. Septiani, “Komparasi Algoritma Data Mining Dalam Prediksi Keamanan Website.”
Sunge, Aswan S, “Prediksi Kompetensi Karyawan Menggunakan Algoritma C4.5 (Studi Kasus : PT Hankook Tire Indonesia)” Seminar Nasional Teknologi Informasi dan Komunikasi 2018 (SENTIKA 2018). ISSN: 2089-9815