An Intelligent Gestational Diabetes Mellitus Recognition System Using Machine Learning Algorithms
Article Sidebar
Main Article Content
Abstract
Diabetes mellitus is also called gestational diabetes when a woman has high blood sugar while she is pregnant. It can show up at any time during pregnancy and cause problems for the mother and baby during or after the pregnancy. If the risks are found and dealt with as soon as possible, there is a chance that they can be reduced. The healthcare system is one of the many parts of our daily lives that are being rethought thanks to the creation of intelligent systems by machine learning algorithms. In this article, a hybrid prediction model is suggested as a way to find out if a woman has gestational diabetes. In the recommended model, the amount of data is reduce by using the K-means clustering method. Predictions are made using a number of classification methods, such as decision tree, random forests, SVM, KNN, logistic regression, and naive bayes. The results show that accuracy goes up when clustering and classification are used together.
Article Details
This work is licensed under a Creative Commons Attribution 4.0 International License.
Tikrit Journal of Pure Science is licensed under the Creative Commons Attribution 4.0 International License, which allows users to copy, create extracts, abstracts, and new works from the article, alter and revise the article, and make commercial use of the article (including reuse and/or resale of the article by commercial entities), provided the user gives appropriate credit (with a link to the formal publication through the relevant DOI), provides a link to the license, indicates if changes were made, and the licensor is not represented as endorsing the use made of the work. The authors hold the copyright for their published work on the Tikrit J. Pure Sci. website, while Tikrit J. Pure Sci. is responsible for appreciate citation of their work, which is released under CC-BY-4.0, enabling the unrestricted use, distribution, and reproduction of an article in any medium, provided that the original work is properly cited.
References
[1] Al-Zebari, A. and Sengur, A. (2019) 'Performance Comparison of Machine Learning Techniques on Diabetes Disease Detection', 1st International Informatics and Software Engineering Conference: Innovative Technologies for Digital Transformation, IISEC 2019 - Proceedings, pp. 2–5. doi: 10.1109/UBMYK48245.2019.8965542.
[2] Alapati, Y. and Sindhu, K. (2016) 'Combining Clustering with Classification: A Technique to Improve Classification Accuracy', International
Journal of Computer Science Engineering, 5(06), pp. 336–338. Available at: https://en.wikipedia.org/wiki/Feature_selection.
[3] Alehegn, M., Joshi, R. and Alehegn, M. (2017) 'Analysis and prediction of diabetes diseases using machine learning algorithm: Ensemble approach.', International Research Journal of Engineering and Technology, 4(10), pp. 426–436. Available at: www.irjet.net.
[4] Ali, N. et al. (2021) 'Effect of gestational diabetes mellitus history on future pregnancy behaviors: The Mutaba'ah study', International Journal of Environmental Research and Public Health, 18(1), pp. 1–12. doi: 10.3390/ijerph18010058.
[5] Hamad, A., Aminifar, S. and Daneshwar, M. (2020) 'An interval type-2 FCM for color image segmentation', International Journal of Advanced Computer Research, 10(46), pp. 12-17. doi: 10.19101/ijacr.2019.940114.
[6] Jader, R.F., Abd, M.H.M. and Jumaa, I.H., 2022. Signal Modulation Recognition System Based on Different Signal Noise Rate Using Artificial Intelligent Approach. Journal of Studies in Science and Engineering, 2(4), pp.37-49.
[7] AlJarullah, A. A. (2011)' Decision tree discovery for the diagnosis of type II diabetes', 2011 International Conference on Innovations in Information Technology, IIT 2011, pp. 303–307. doi: 10.1109/INNOVATIONS.2011.5893838.
[8] Barakat, N., Bradley, A. P. and Barakat, M. N. H. (2010) 'Intelligible support vector machines for diagnosis of diabetes mellitus', IEEE Transactions on Information Technology in Biomedicine, 14(4), pp. 1114–1120. doi: 10.1109/TITB.2009.2039485.
[9] Aminifar, S. and Marzuki, A. (2013) 'Uncertainty in interval type-2 fuzzy systems', Mathematical Problems in Engineering, 2013. doi: 10.1155/2013/452780.
[10] Benbelkacem, S. and Atmani, B. (2019) 'Random forests for diabetes diagnosis', 2019 International Conference on Computer and Information Sciences, ICCIS 2019, pp. 1–4. doi: 10.1109/ICCISci.2019.8716405.
[11] Chen, W. et al. (2018) 'A hybrid prediction model for type 2 diabetes using K-means and decision tree', Proceedings of the IEEE International Conference on Software Engineering and Service Sciences, ICSESS, 2017-Novem(61272399), pp. 386–390. doi: 10.1109/ICSESS.2017.8342938.
[12] Aminifar, S., 2014. Design and implementation of fuzzy controllers for handling uncertainty in an industrial application (Doctoral dissertation, Universiti Sains Malaysia).
[13] Jader, R.F., Aminifar, S. and Abd, M.H.M., 2022. Diabetes Detection System by Mixing Supervised and Unsupervised Algorithms. Journal of Studies in Science and Engineering, 2(3), pp.52-65.
[14] Aminifar, S. (2020) ‘Uncertainty Avoider Interval Type II Defuzzification Method’, Mathematical Problems in Engineering. Edited by J. V. Salcedo, 2020, p. 5812163. doi: 10.1155/2020/5812163.
[15] Conway, D. L. (2012) 'Gestational Diabetes Mellitus', Queenan's Management of High-Risk Pregnancy: An Evidence-Based Approach: Sixth Edition, 26, pp. 168–173. doi: 10.1002/9781119963783.ch20.
[16] Gnanadass, I. (2020) 'Prediction of Gestational Diabetes by Machine Learning Algorithms', IEEE Potentials, 39(6), pp. 32–37. doi: 10.1109/MPOT.2020.3015190.
[17] Jeevan Nagendra Kumar, Y. et al. (2019) 'Prediction of diabetes using machine learning', International Journal of Innovative Technology and Exploring Engineering, 8(7), pp. 2547–2551. doi: 10.35940/ijrte.e6290.018520.
[18] Jiang, F. et al. (2017) 'Artificial intelligence in healthcare: Past, present and future', Stroke and Vascular Neurology, 2(4), pp. 230–243. doi: 10.1136/svn-2017-000101.
[19] Likas, A., Vlassis, N. and Verbeek, J. (2011) 'The global k-means clustering algorithm Intelligent Autonomous Systems', ISA technical report series.
[20] Marzuki, A., Tee, S. Y. and Aminifar, S. (2014) 'Study of fuzzy systems with Sugeno and Mamdanitype fuzzy inference systems for determination of heartbeat cases on Electrocardiogram (ECG) signals', International Journal of Biomedical Engineering and Technology, 14(3), pp. 243-276. doi: 10.1504/IJBET.2014.059673.
[21] Rasool Jader, Sadegh Aminifar, "Predictive Model for Diagnosis of Gestational Diabetes in the Kurdistan Region by a Combination of Clustering and Classification Algorithms: An Ensemble Approach", Applied Computational Intelligence and Soft Computing, vol. 2022, Article ID 9749579, 11 pages, 2022. https://doi.org/10.1155/2022/9749579
[22] Patro, S. G. K. and sahu, K. K. (2015) 'Normalization: A Pre-processing Stage', Iarjset, pp. 20–22. doi: 10.17148/iarjset.2015.2305.
[23] Saravana Kumar, N. M. et al. (2015) 'Predictive methodology for diabetic data analysis in big data', Procedia Computer Science, 50, pp. 203–208. doi: 10.1016/j.procs.2015.04.069.
[24] Sarwar, M. A. et al. (2018) 'Prediction of diabetes using machine learning algorithms in healthcare', ICAC 2018 - 2018 24th IEEE International Conference on Automation and Computing: Improving Productivity through Automation and Computing, (September), pp. 1–6. doi: 10.23919/IConAC.2018.8748992.
[25] Jader, R. and Aminifar, S., 2022. Fast and Accurate Artificial Neural Network Model for Diabetes Recogni-tion. NeuroQuantology, 20(10), pp.2187-2196.
[26] Aminifar, S. and Bin Marzuki, A. (2013) 'Horizontal and vertical rule bases method in fuzzy controllers', Mathematical Problems in Engineering, 2013. doi: 10.1155/2013/532046.
[27] Sinaga, K. P. and Yang, M. S. (2020) 'Unsupervised K-means clustering algorithm', IEEE Access, 8, pp. 80716–80727. doi: 10.1109/ACCESS.2020.2988796.
[28] Sonar, P. and Jaya Malini, K. (2019) 'Diabetes prediction using different machine learning approaches', Proceedings of the 3rd International
Conference on Computing Methodologies and Communication, ICCMC 2019, (Iccmc), pp. 367–371. doi: 10.1109/ICCMC.2019.8819841.
[29] Vijayan, V. V. and Anjali, C. (2016) 'Prediction and diagnosis of diabetes mellitus - A machine learning approach', 2015 IEEE Recent Advances in Intelligent Computational Systems, RAICS 2015, (December), pp. 122–127. doi: 10.1109/RAICS.2015.7488400.
[30] Brereton, R. G. (2015) ‘The mahalanobis distance and its relationship to principal component scores’, Journal of Chemometrics, 29(3), pp. 143–145. doi: 10.1002/cem.2692.
[31] Tuysuzoglu, G., Birant, D. and Pala, A. (2019) ‘Majority voting based multi-task clustering of air quality monitoring network in Turkey’, Applied
Sciences (Switzerland), 9(8), pp. 1–21. doi: 10.3390/app9081610.
[32] Jader, R.F., Aminifar, S. and Abd, M.H.M., 2022. Journal of Studies in Science and Engineering.
[33] Bansal, M., Goyal, A. and Choudhary, A. (2022) ‘A comparative analysis of K-Nearest Neighbor, Genetic, Support Vector Machine, Decision Tree, and Long Short Term Memory algorithms in machine learning’, Decision Analytics Journal, 3(May), p. 100071. doi: 10.1016/j.dajour.2022.100071.
[34] Chung, J. and Teo, J. (2022) ‘Mental Health Prediction Using Machine Learning: Taxonomy, Applications, and Challenges’, Applied Computational Intelligence and Soft Computing. Edited by A. Minutolo, 2022, p. 9970363. doi: 10.1155/2022/9970363.