Cloud-Edge Collaborative Computing Framework for Stroke Disease Classification Using Machine Learning - I Made Suartana; Ricky Eka Putra; Rahadian Bisma (Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA, 2026)
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