Diabetes Prediction Using Machine Learning Classifiers
Author:
Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-62217-5_12
Reference10 articles.
1. Marzouk, R., Alluhaidan, A.S., El_Rahman, S.A.: An analytical predictive models and secure web-based personalized diabetes monitoring system. IEEE Access 10, 105657–105673 (2022)
2. Philip, N.Y., Razaak, M., Chang, J., O’Kane, M., Pierscionek, B.K., et al.: A data analytics suite for exploratory predictive, and visual analysis of type 2 diabetes. IEEE Access 10, 13460–13471 (2022)
3. Islam, M.S., Qaraqe, M.K., Belhaouari, S.B., Abdul-Ghani, M.A.: Advanced techniques for predicting the future progression of type 2 diabetes. IEEE access 8, 120537–120547 (2020)
4. Annuzzi, G., et al.: Impact of nutritional factors in blood glucose prediction in type 1 diabetes through machine learning. IEEE Access 11, 17104–17115 (2023)
5. Theis, J., Galanter, W.L., Boyd, A.D., Darabi, H.: Improving the in-hospital mortality prediction of diabetes ICU patients using a process mining/deep learning architecture. IEEE J. Biomed. Health Inform. 26(1), 388–399 (2021)
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