Diabetes Detection System by Mixing Supervised and Unsupervised Algorithms
Article Sidebar
Main Article Content
Abstract
Diabetes mellitus is also called gestational diabetes when a woman has high blood sugar while 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 to determine if a woman has gestational diabetes. The recommended model reduces the amount of data using the K-means clustering method. Predictions are made using several classification methods, such as decision trees, random forests, SVM, KNN, logistic regression, and naive Bayes. The results show that accuracy increases when clustering and classification are used together.
Article Details
This work is licensed under a Creative Commons Attribution 4.0 International License.
Journal of Studies in Science and Engineering is licensed under a Creative Commons Attribution License 4.0 (CC BY-4.0).
References
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.
Jader, R. and Aminifar, S., 2022. Fast and Accurate Artificial Neural Network Model for Diabetes Recogni-tion. NeuroQuantology, 20(10), pp.2187-2196.
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.
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.
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.
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.
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.
Benbelkacem, S. and Atmani, B. (2019) 'Random forests for diabetes diagnosis', 2019 International Conference on Computer and Information Sciences, ICCIS 2019, pp. 1-4.
Choudhury, A. and Gupta, D. (2019) A Survey on Medical Diagnosis of Diabetes Using Machine Learning Tech-niques, Advances in Intelligent Systems and Computing. Springer Singapore.
Conway, D. L. (2012) 'Gestational Diabetes Mellitus', Queenan's Management of High-Risk Pregnancy: An Evi-dence-Based Approach: Sixth Edition, 26, pp. 168-173.
Gnanadass, I. (2020) 'Prediction of Gestational Diabetes by Machine Learning Algorithms', IEEE Potentials, 39(6), pp. 32-37.
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.
Jiang, F. et al. (2017) 'Artificial intelligence in healthcare: Past, present and future', Stroke and Vascular Neurology, 2(4), pp. 230-243.
Likas, A., Vlassis, N. and Verbeek, J. (2011) 'The global k-means clustering algorithm Intelligent Autonomous Sys-tems', ISA technical report series.
Mujumdar, A. and Vaidehi, V. (2019) 'Diabetes Prediction using Machine Learning Algorithms', Procedia Computer Science, 165, pp. 292-299.
Patro, S. G. K. and sahu, K. K. (2015) 'Normalization: A Preprocessing Stage', Iarjset, pp. 20-22.
Saravana Kumar, N. M. et al. (2015) 'Predictive methodology for diabetic data analysis in big data', Procedia Com-puter Science, 50, pp. 203-208.
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.
Sinaga, K. P. and Yang, M. S. (2020) 'Unsupervised K-means clustering algorithm', IEEE Access, 8, pp. 80716-80727.
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.
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.
H. Q. Awla, A. Rahman Mirza and S. W. Kareem, "An Automated CAPTCHA for Website Protection Based on User Behavioral Model," 2022 8th International Engineering Conference on Sustainable Technology and Development (IEC), 2022, pp. 161-167.
Awla, H.Q., Kareem, S.W. and Mohammed, A.S., 2022. Bayesian Network Structure Discovery Using Antlion Opti-miza-tion Algorithm. International Journal of Systematic Innovation, 7(1), pp.46-65.
Mirza, A.R. and Sah, M., 2017. Automated software system for checking the structure and format of ACM SIG documents. New Review of Hypermedia and Multimedia, 23(2), pp.112-140.
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.
Aminifar, S. and Marzuki, A. (2013) 'Uncertainty in interval type-2 fuzzy systems', Mathematical Problems in Engineering, 2013.
Aminifar, S., 2014. Design and implementation of fuzzy controllers for handling uncertainty in an industrial application (Doctoral dissertation, Universiti Sains Malaysia).
Aminifar, S. (2020) 'Uncertainty Avoider Interval Type II Defuzzification Method', Mathematical Problems in Engineering. Edited by J. V. Salcedo, 2020, p. 5812163.
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.
Aminifar, S. and Bin Marzuki, A. (2013) 'Horizontal and vertical rule bases method in fuzzy controllers', Mathematical Problems in Engineering, 2013.