Open Access
| Issue |
ITM Web Conf.
Volume 32, 2020
International Conference on Automation, Computing and Communication 2020 (ICACC-2020)
|
|
|---|---|---|
| Article Number | 03032 | |
| Number of page(s) | 7 | |
| Section | Computing | |
| DOI | https://doi.org/10.1051/itmconf/20203203032 | |
| Published online | 29 July 2020 | |
- Mukesh kumari, Anshul arora ,Dr.Rajan Vohra, “Pre-diction of Diabetes Using Bayesian Network”, International Journal of Computer Science and Information Technologies ,2014. [Google Scholar]
- S. Wei, X. Zhao, and** C. Miao, “A comprehensive exploration to the machine learning techniques for diabetes identification,” 2018 IEEE 4th World Forum on Internet of Things (WFIoT), 2018. [Google Scholar]
- W. Chen, S. Chen, H. Zhang, and** T. Wu, “A hybrid prediction model for type 2 diabetes using K-means and decision tree,” 2017 8th IEEE International Conference on Software Engineering and Service Science (ICSESS), 2017. [Google Scholar]
- S.Y. Rubaiat, M.M. Rahman, and** M.K. Hasan, “Important Feature Selection Accuracy Comparisons of Different Machine Learning Models for Early Diabetes Detection,” 2018 International Conference on Innovation in Engineering and Technology (ICIET), 2018. [Google Scholar]
- M. Dutt, V. Nunavath, and** M. Goodwin, “A Multilayer Feed Forward Neural Network Approach for Diagnosing Diabetes,” 2018 11th International Conference on Developments in eSystems Engineering (DeSE), 2018. [Google Scholar]
- Barakat, N., Bradley, A.P., Barakat, M.N.H. “Intelligible Support Vector Machines for Diagnosis of Diabetes Mellitus”. IEEE Transactions on Information Technology in Biomedicine, 14(4), 1114-1120 (2010). [CrossRef] [Google Scholar]
- Han, L., Luo, S., Yu, J., Pan, L. Chen, S. “Rule Extraction From Support Vector Machines Using Ensemble Learning Approach: An Application for Diagnosis of Diabetes “. IEEE Journal of Biomedical and Health Informatics 19, 728-734 (2015). [Google Scholar]
- Nongyao Nai-aruna, Rungruttikarn Moung-maia, ”Comparison of Classifiers for the Risk of Diabetes Prediction” 7th International Conference on Advances in Information Technology,2015. [Google Scholar]
- M.A. Helal, A.I. Chowdhury, A. Islam, E. Ahmed, M.S. Mahmud, and** S. Hossain, “An Optimization Approach to Improve Classification Performance in Cancer and Diabetes Prediction,” 2019 International Conference on Electrical, Computer and Communication Engineering (ECCE), 2019. [Google Scholar]
- Lekha, S. Suchetha, M. “A Novel 1-D Convolution Neural Network With SVM Architecture for Real-Time Detection Applications”. IEEE Sensors Journal 18, 724-731 (2018). [Google Scholar]
- Mustafa S. Kadhm, Ikhlas Watan Ghindawi, Duaa En-teesha Mhawi”An Accurate Diabetes Prediction System Based on K-means Clustering and Proposed Classification Approach” International Journal of Applied Engineering Research ISSN 0973-4562 volume 13, Number 6 (2018) [Google Scholar]
- Temurtas, H., Yumusak, N. Temurtas, F. “A comparative study on diabetes disease diagnosis using neural networks”. Expert Systems with Applications 36, 8610-8615 (2009). [Google Scholar]
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