Diabetes Mellitus, one of the leading causes of death worldwide, has no cure till date and can lead to severe health complications if left untreated. Consequently, it becomes crucial to take precautionary measures to avoid/predict the occurrence of diabetes. Machine learning approaches have been proposed and evaluated in the literature for diabetes prediction. However, there exists no end-to-end diabetes prediction system. This work proposes an Artificial Intelligence (AI)-blockchain system for diabetes prediction based on risk factors. The proposed system is underpinned by the IoT-Edge-Cloud Computing and blockchain to obtain a cohesive view of the risk factors data from patients across different hospitals and to ensure security and privacy of the user’s data.
The major conrributions of this work are as follows:
- A end-to-end framework for the monitoring of risk factors and prediction of diabetes for users.
- Design and Architecture of an end-to-end system for monitoring and prediction of diabetes risk.
- Development of a prototype for diabetes risk factors monitoring and prediction in IoT-Edge integrated system..
- Implementation of an authentication mechanism and a block-chain technique to ensure that data is protected.
- Performacne and results analysis of our system using the mostly used approaches of machine learning for diabetes prediction in the literature.
Hennebelle, Alain, Huned Materwala, and Leila Ismail. "HealthEdge: a machine learning-based smart healthcare framework for prediction of type 2 diabetes in an integrated IoT, edge, and cloud computing system." Procedia Computer Science 220 (2023): 331-338. https://www.sciencedirect.com/science/article/pii/S1877050923005781
Ismail, Leila, Alain Hennebelle, Huned Materwala, Juma Al Kaabi, Priya Ranjan, and Rajiv Janardhanan. "Secure and Privacy-Preserving Automated End-to-End Integrated IoT-Edge-Artificial Intelligence-Blockchain Monitoring System for Diabetes Mellitus Prediction." arXiv preprint arXiv:2211.07643 (2022). https://arxiv.org/abs/2211.07643
- Leila Ismail, Alain Hennebelle, Huned Materwalaa, Juma Al Kaabie, Priya Ranjan, Rajiv Janardhanan, Secure and Privacy-Preserving Automated End-to-End Integrated IoT-Edge-Artificial Intelligence-Blockchain Monitoring System for Diabetes Mellitus Prediction (Coming soon ...)
- Ismail, L., Materwala, H., Tayefi, M. et al. Type 2 Diabetes with Artificial Intelligence Machine Learning: Methods and Evaluation. Arch Computat Methods Eng 29, 313–333 (2022). https://doi.org/10.1007/s11831-021-09582-x
- Ismail, Leila, and Huned Materwala. "IDMPF: intelligent diabetes mellitus prediction framework using machine learning." Applied Computing and Informatics (2021). https://www.emerald.com/insight/content/doi/10.1108/ACI-10-2020-0094/full/html
- Leila Ismail, Huned Materwala, Juma Al Kaabi, Association of risk factors with type 2 diabetes: A systematic review, Computational and Structural Biotechnology Journal, Volume 19, 2021, Pages 1759-1785, ISSN 2001-0370, https://doi.org/10.1016/j.csbj.2021.03.003.
- L. Ismail and H. Materwala, "Comparative Analysis of Machine Learning Models for Diabetes Mellitus Type 2 Prediction," 2020 International Conference on Computational Science and Computational Intelligence (CSCI), 2020, pp. 527-533, doi: 10.1109/CSCI51800.2020.00095.