PREDICTION OF GLUCOSE LEVEL IN DIABETICS WITH SUPPORT VECTOR REGRESSION
Prediksi Level Glukosa pada Penderita Diabetes dengan Support Vector Regression
One of the common diabetes factors that people hear is that they consume too much or often consume sweet foods or drinks so that blood sugar in the human body increases. The times and increasingly sophisticated technology make it easier for someone to be able to predict a disease such as diabetes with machine learning techniques. Therefore, from the existing problems, a machine learning technique will be made in predicting glucose levels in diabetics. The aim is to predict glucose levels in diabetics and find the best algorithm from several comparison algorithms. The results of the experiments carried out by the support vector regression algorithm have a lower mean squared error value of 28.9480 compared to other comparative algorithms and visualize the error classification seen that Instance no 47 has a prediction of the highest plasma glucose value of 189.2305.
Al-Khasawneh, A., & Hijazi, H. (2014). A Predictive E-Health Information System: Diagnosing Diabetes Mellitus Using Neural Network Based Decision Support System. International Journal of Decision Support System Technology, 6(4), 31–48. https://doi.org/10.4018/ijdsst.2014100103
Alloghani, M., Aljaaf, A., Hussain, A., Baker, T., Mustafina, J., Al-Jumeily, D., & Khalaf, M. (2019). Implementation of machine learning algorithms to create diabetic patient re-admission profiles. BMC Medical Informatics and Decision Making, 19(Suppl 9), 253. https://doi.org/10.1186/s12911-019-0990-x
Anggraini, A. (2019). 10 Efek Buruk Makanan Manis Pada Tubuh. Https://Www.Nibble.Id/. https://www.nibble.id/blog/10-efek-buruk-makanan-manis-pada-tubuh/
Belinda, G. (2019). Kadar Gula Darah Normal dan Cara Mencegah Diabetes. Honestdocs. https://www.honestdocs.id/kadar-gula-darah-normal
Kahn, M. (1994). Diabetes Data Set. UCI Machine Learning Repository. https://archive.ics.uci.edu/ml/datasets/diabetes
Kaur, H., & Kumari, V. (2018). Predictive modelling and analytics for diabetes using a machine learning approach. Applied Computing and Informatics, 1–6. https://doi.org/10.1016/j.aci.2018.12.004
Kavakiotis, I., Tsave, O., Salifoglou, A., Maglaveras, N., Vlahavas, I., & Chouvarda, I. (2017). Machine Learning and Data Mining Methods in Diabetes Research. In Computational and Structural Biotechnology Journal (Vol. 15, pp. 104–116). Elsevier B.V. https://doi.org/10.1016/j.csbj.2016.12.005
Mukarromah, M., Martha, S., & Ilhamsyah, I. (2015). PERBANDINGAN IMPUTASI MISSING DATA MENGGUNAKAN METODE MEAN DAN METODE ALGORITMA K-MEANS. BIMASTER, 4(3), 305–312. http://jurnal.untan.ac.id/index.php/jbmstr/article/view/12425/
Pappada, S. M., Cameron, B. D., Rosman, P. M., Bourey, R. E., Papadimos, T. J., Olorunto, W., & Borst, M. J. (2011). Neural network-based real-time prediction of glucose in patients with insulin-dependent diabetes. Diabetes Technology and Therapeutics, 13(2), 135–141. https://doi.org/10.1089/dia.2010.0104
Robertson, G., Lehmann, E. D., Sandham, W., & Hamilton, D. (2011). Blood Glucose Prediction Using Artificial Neural Networks Trained with the AIDA Diabetes Simulator: A Proof-of-Concept Pilot Study. Journal of Electrical and Computer Engineering, 2011, 1–11. https://doi.org/doi:10.1155/2011/681786
Sandham, W. A., Lehmann, E. D., Zequera Diaz, M., Hamilton, D. J., Tatti, P., & Walsh, J. (2011). Electrical and computer technology for effective diabetes management and treatment. Journal of Electrical and Computer Engineering, 2011, 1–3. https://doi.org/10.1155/2011/289359
Saputra, N., Adji, T. B., & Permanasari, A. E. (2016). Analisis sentimen data presiden Jokowi dengan preprocessing normalisasi dan stemming menggunakan metode naive bayes dan SVM. Jurnal Dinamika Informatika, 5(1), 1–12. http://ojs.upy.ac.id/ojs/index.php/dinf/article/view/113
Sarojini, B., Ramaraj, N., & Nickolas, S. (2009). Enhancing the performance of libSVM classifier by kernel f-score feature selection. Communications in Computer and Information Science, 40, 533–543. https://doi.org/10.1007/978-3-642-03547-0_51
Soumya, D., & Srilatha, B. (2011). Late Stage Complications of Diabetes and Insulin Resistance. Journal of Diabetes & Metabolism, 02(09), 1–8. https://doi.org/10.4172/2155-6156.1000167
Sun, S. (2013). A survey of multi-view machine learning. In Neural Computing and Applications (Vol. 23, Issues 7–8, pp. 2031–2038). Springer. https://doi.org/10.1007/s00521-013-1362-6
Waila, P., Marisha, S., Singh, V. K., & Singh, M. K. (2012). Evaluating Machine Learning and Unsupervised Semantic Orientation approaches for sentiment analysis of textual reviews. 2012 IEEE International Conference on Computational Intelligence and Computing Research, ICCIC 2012. https://doi.org/10.1109/ICCIC.2012.6510235
Wulandari, D., & Subekti, A. (2019). Laporan Akhir Penelitian Mandiri: Prediksi Level Glukosa Pada Penderita Diabetes Dengan Support Vector Regression.
Abstract viewed = 150 times
PDF downloaded = 83 times
Copyright (c) 2020 Devi Wulandari, Agus Subekti
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
The Authors submitting a manuscript do so on the understanding that if accepted for publication, copyright of the article shall be assigned to the PILAR Nusa Mandiri journal as the publisher of the journal, and the author also holds the copyright without restriction.
Copyright encompasses exclusive rights to reproduce and deliver the article in all form and media, including reprints, photographs, microfilms, and any other similar reproductions, as well as translations. The reproduction of any part of this journal, its storage in databases, and its transmission by any form or media, such as electronic, electrostatic and mechanical copies, photocopies, recordings, magnetic media, etc. , are allowed with written permission from the PILAR Nusa Mandiri journal.
PILAR Nusa Mandiri journal, the Editors and the Advisory International Editorial Board make every effort to ensure that no wrong or misleading data, opinions, or statements be published in the journal. In any way, the contents of the articles and advertisements published in the PILAR Nusa Mandiri journal are the sole and exclusive responsibility of their respective authors and advertisers.