Diterbitkan Oleh:
Lembaga Penelitian Pengabdian Masyarakat Universitas Nusa Mandiri
Creation is distributed below Lisensi Creative Commons Atribusi-NonKomersial 4.0 Internasional.
PREDICTION OF GLUCOSE LEVEL IN DIABETICS WITH SUPPORT VECTOR REGRESSION
Prediksi Level Glukosa pada Penderita Diabetes dengan Support Vector Regression
Keywords:
Diabetes, Glukosa Level, Support Vector Regression
Abstract
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.
Downloads
Download data is not yet available.
References
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.
Published
2020-03-31
How to Cite
Wulandari, D., & Subekti, A. (2020). PREDICTION OF GLUCOSE LEVEL IN DIABETICS WITH SUPPORT VECTOR REGRESSION. Jurnal Pilar Nusa Mandiri, 16(1), 97-102. https://doi.org/10.33480/pilar.v16i1.1264
Section
Articles
Copyright (c) 2020 Devi Wulandari, Agus Subekti
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
An author who publishes in the Pilar Nusa Mandiri: Journal of Computing and Information System agrees to the following terms:
- Author retains the copyright and grants the journal the right of first publication of the work simultaneously licensed under the Creative Commons Attribution-NonCommercial 4.0 License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal
- Author is able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book) with the acknowledgement of its initial publication in this journal.
- Author is permitted and encouraged to post his/her work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of the published work (See The Effect of Open Access).
Read more about the Creative Commons Attribution-NonCommercial 4.0 Licence here: https://creativecommons.org/licenses/by-nc/4.0/.
