Bài báo: “A Home-based Diabetes Prediction System on Internet of Things, Federated Learning and Edge Computing”
Học viên thực hiện:
- Huỳnh Phi Long - MMCL 2020 - Tác giả chính
- Nguyễn Khánh Duy - MMCL 2020 - Đồng tác giả
Giáo viên hướng dẫn:
- ThS. Nguyễn Khánh Thuật
- ThS. Đặng Lê Bảo Chương
- PGS. TS. Lê Trung Quân
Tóm tắt bài báo:
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.
Thông tin chung:
You are cordially invited to participate in and attend the 10th EAI International Conference on Industrial Networks and Intelligent Systems (INISCOM 2024) to be held in Da Nang City, Vietnam on Feb. 20-21, 2024. The conference is to address, explore and exchange information on the state-of-the-art in all types of Big Data, AI, Digital Twin and 6G Networks: Technologies, Services and Applications. We are interested in visionary, experimental, systems-related and work in-progress papers on the current state of research. Papers should describe original, previously unpublished work, not currently under review by another conference, workshop, or journal.
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