Research Article
A Home-Based Diabetes Prediction System on Internet of Things, Federated Learning and Edge Computing
@INPROCEEDINGS{10.1007/978-3-031-67357-3_2, author={Long Huynh-Phi and Duy Nguyen-Khanh and Thuat Nguyen-Khanh and Chuong Dang-Le-Bao and Quan Le-Trung}, title={A Home-Based Diabetes Prediction System on Internet of Things, Federated Learning and Edge Computing}, proceedings={Industrial Networks and Intelligent Systems. 10th EAI International Conference, INISCOM 2024, Da Nang, Vietnam, February 20--21, 2024, Proceedings}, proceedings_a={INISCOM}, year={2024}, month={7}, keywords={Home-based health system Internet of Things Federated learning Edge computing Diabetes prediction}, doi={10.1007/978-3-031-67357-3_2} }- Long Huynh-Phi
Duy Nguyen-Khanh
Thuat Nguyen-Khanh
Chuong Dang-Le-Bao
Quan Le-Trung
Year: 2024
A Home-Based Diabetes Prediction System on Internet of Things, Federated Learning and Edge Computing
INISCOM
Springer
DOI: 10.1007/978-3-031-67357-3_2
Abstract
Detecting the disease early is an important step in reducing its impact. In recent years, applications that monitor and predict health metrics using machine learning have attracted public attention. Our research built a diabetes health monitoring and prediction system based on the Edge Computing model. For hospitals, users and patients are represented by K3 clusters. The K-Nearest Neighbor (KNN) algorithm is run in a distributed fashion using Federated Learning with the proposed system. It could allow people to track their vital health indicators without having to go to the hospital. In our proposed system, the diabetes risk level can be predicted in advance so that users can take preventive steps. Through Federated Learning used, the model is being trained on distributed data sources guaranteed to preserve privacy and improve accuracy. K-Nearest Neighbor in the federated learning cluster, we can improve the prediction accuracy by up to 10% compared to the standalone version of K-Nearest Neighbor.
