Open Access
| Issue |
E3S Web Conf.
Volume 699, 2026
11th International Conference on Energy and City of the Future (EVF’2024)
|
|
|---|---|---|
| Article Number | 05003 | |
| Number of page(s) | 13 | |
| Section | E-Health, Professions of the Future and Related Training Courses | |
| DOI | https://doi.org/10.1051/e3sconf/202669905003 | |
| Published online | 20 March 2026 | |
- World Health Organization (WHO). Rapport Mondial sur le Diabète. IRIS Home, (2016). Available at: https://iris.who.int/server/api/core/bitstreams/3a7bd54a-ac19-4d33-a64f-a122612c13da/content [Google Scholar]
- A. Mir, S. N. Dhage. Diabetes disease prediction using machine learning on big data of healthcare. In 2018 Fourth International Conference on Computing Communication Control and Automation (ICCUBEA), IEEE, (2018), pp. 1-6. https://doi.org/10.1109/ICCUBEA.2018.8697439 [Google Scholar]
- M. Humayun, M. I. Khalil, G. Alwakid, N. Z. Jhanjhi. Superlative Feature Selection Based Image Classification Using Deep Learning in Medical Imaging. J. Healthc. Eng. 2022, 8717027 (2022). https://doi.org/10.1155/2022/7028717 [Google Scholar]
- S. Lundberg, S. Lee. A unified approach to interpreting model predictions. Adv. Neural Inf. Process. Syst. 30, 4768–4777 (2017) [Google Scholar]
- M. T. Ribeiro, S. Singh, C. Guestrin. “Why should i trust you?” Explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA, August 13-17 (2016), pp. 1135-1144. https://doi.org/10.1145/2939672.2939778 [Google Scholar]
- J. W. Smith, J. E. Everhart, W. C. Dickson, W. C. Knowler, R. S. Johannes. Using the ADAP Learning Algorithm to Forecast the Onset of Diabetes Mellitus. Proc. Annu. Symp. Comput. Appl. Med. Care 9, 261–265 (1988). https://pmc.ncbi.nlm.nih.gov/articles/PMC2245318/ [Google Scholar]
- J. Ling Yuan, et al. Machine Learning Approach in Medical Diagnosis: Predicting Diabetes Complications. J. Appl. Technol. Innov. 8(3), 52 (2024). https://doi.org/10.65136/jati.v8i3.89 [Google Scholar]
- T. Zheng, et al. A machine learning-based framework to identify type 2 diabetes through electronic health records. Int. J. Med. Inform. 97, 120-127 (2017). https://doi.org/10.1016/j.ijmedinf.2016.09.014 [Google Scholar]
- X. ZiDian, et al. Building risk prediction models for type 2 diabetes using machine learning techniques. Prev. Chronic Dis. 16, 190109 (2019). http://dx.doi.org/10.5888/pcd16.190109 [Google Scholar]
- M. F. Islam, R. Ferdousi, S. Rahman, H. Y. Bushra. Likelihood prediction of diabetes at early-stage using data mining techniques. In Computer Vision and Machine Intelligence in Medical Image Analysis: International Symposium, ISCMM 2019, Singapore, (2020), pp. 113-125. https://doi.org/10.1007/978-981-13-8798-2_12 [Google Scholar]
- A. R. Panda, P. Rout, A. Gautam. Behavioural risk factor surveillance of diabetes using supervised learning. In 2024 International Conference on Advances in Computing Research on Science Engineering and Technology (ACROSET), IEEE, (2024), pp. 1-6. https://doi.org/10.1109/ACROSET62108.2024.10743921 [Google Scholar]
- A. Rashid. Diabetes dataset. Mendeley Data, V1 (2020). Available at: https://data.mendeley.com/datasets/wj9rwkp9c2/1 [Google Scholar]
- B. Strack, et al. Impact of HbA1c measurement on hospital readmission rates: analysis of 70,000 clinical database patient records. Biomed Res. Int. 2014(1), 781670 (2014). https://doi.org/10.1155/2014/781670 [Google Scholar]
- R. El Kharoua. Diabetes Health Dataset [Data set]. Kaggle, (2024). https://doi.org/10.34740/kaggle/dsv/8665939 [Google Scholar]
- G. Eknoyan, et al. Proteinuria and other markers of chronic kidney disease: a position statement of the national kidney foundation (NKF) and the national institute of diabetes and digestive and kidney diseases (NIDDK). Am. J. Kidney Dis. 42(4), 617-622 (2003). Available at: https://www.ajkd.org/article/S0272-6386(03)00826-6/fulltext [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.