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All Journal Journal of Electronics, Electromedical Engineering, and Medical Informatics
I Made Suartana
Department of Informatics, Universitas Negeri Surabaya, Surabaya, Indonesia
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

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Cloud-Edge Collaborative Computing Framework for Stroke Disease Classification Using Machine Learning I Made Suartana; Ricky Eka Putra; Rahadian Bisma
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 8 No 3 (2026): July
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v8i3.1702

Abstract

Stroke is the second leading cause of death and the third leading cause of disability worldwide. Artificial intelligence-based early detection in distributed environments faces three main obstacles: high latency in centralized cloud approaches, risks to patient data privacy during data transmission, and class imbalance in stroke datasets. This study proposes a three-layer collaborative computing framework, Cloud-Edge Collaborative Computing (CECC), which intelligently distributes the computational workload between edge nodes and the cloud for IoMT-based stroke risk classification. The primary novelty of this study lies in the hierarchical computing collaboration that enables real-time preprocessing at the edge layer, centralized model training at the cloud layer, and a local differential privacy mechanism (LDP, ε=0.5) that preserves patient data confidentiality during transmission, all entirely evaluated within a single unified multi-criterion benchmarking protocol. Gradient Boosting achieved the best performance in the hold-out evaluation with an accuracy of 95.01% and an AUC-ROC of 0.994. The CECC framework reduced inference latency by 44.9% (286.2ms to 157.8ms), bandwidth by 73.9% (3,240 to 847 Kbps), and memory by 84.4% (312.4 to 48.7 MB) with an accuracy degradation of only 0.30% compared to cloud only. This study is a simulation-based framework evaluation using a public retrospective dataset prospective clinical validation in a real IoMT environment remains necessary before actual clinical implementation because the dataset used is retrospective, small, highly imbalanced, and was not collected from a real IoMT system
Co-Authors Rahadian Bisma Ricky Eka Putra