Cloud-Edge Collaborative Computing Framework for Stroke Disease Classification Using Machine Learning

I Made Suartana; Ricky Eka Putra; Rahadian Bisma

doi:10.35882/jeeemi.v8i3.1702

I Made Suartana Department of Informatics, Universitas Negeri Surabaya, Surabaya, Indonesia https://orcid.org/0000-0002-2877-5778
Ricky Eka Putra Department of Informatics, Universitas Negeri Surabaya, Surabaya, Indonesia http://orcid.org/0000-0002-5515-7967
Rahadian Bisma Department of Information Systems, Universitas Negeri Surabaya, Surabaya, Indonesia http://orcid.org/0000-0003-0979-5148

DOI: https://doi.org/10.35882/jeeemi.v8i3.1702

Keywords: Cloud-Edge Computing, Stroke Classification, Machine Learning, Random Forest, Internet of Medical Things, Differential Privacy, Class Imbalance

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

Downloads

Download data is not yet available.

References

V. L. Feigin et al., “World Stroke Organization: Global Stroke Fact Sheet 2025,” International Journal of Stroke, vol. 20, no. 2, pp. 132–144, Feb. 2025, doi: 10.1177/17474930241308142.

V. L. Feigin et al., “World Stroke Organization (WSO): Global Stroke Fact Sheet 2022,” International Journal of Stroke, vol. 17, no. 1, pp. 18–29, Jan. 2022, doi: 10.1177/17474930211065917.

V. L. Feigin et al., “Global, regional, and national burden of stroke and its risk factors, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021,” Lancet Neurol., vol. 23, no. 10, pp. 973–1003, Oct. 2024, doi: 10.1016/S1474-4422(24)00369-7.

L. Gupta, “Collaborative Edge-Cloud AI for IoT Driven Secure Healthcare System,” in 2023 IEEE International Systems Conference (SysCon), IEEE, Apr. 2023, pp. 1–8. doi: 10.1109/SysCon53073.2023.10131082.

A. Alzu’bi, A. Alomar, S. Alkhaza’leh, A. Abuarqoub, and M. Hammoudeh, “A Review of Privacy and Security of Edge Computing in Smart Healthcare Systems: Issues, Challenges, and Research Directions,” Tsinghua Sci. Technol., vol. 29, no. 4, pp. 1152–1180, Aug. 2024, doi: 10.26599/TST.2023.9010080.

K. Kitova, I. Ivanov, and V. Hooper, “Stroke Dataset Modeling: Comparative Study of Machine Learning Classification Methods,” Algorithms, vol. 17, no. 12, p. 571, Dec. 2024, doi: 10.3390/a17120571.

E. Dritsas and M. Trigka, “Stroke Risk Prediction with Machine Learning Techniques,” Sensors, vol. 22, no. 13, p. 4670, Jun. 2022, doi: 10.3390/s22134670.

T. Tazin, M. N. Alam, N. N. Dola, M. S. Bari, S. Bourouis, and M. Monirujjaman Khan, “Stroke Disease Detection and Prediction Using Robust Learning Approaches,” J. Healthc. Eng., vol. 2021, pp. 1–12, Nov. 2021, doi: 10.1155/2021/7633381.

M. S. Yaraziz, N. Sohrabi Safa, and M. A. Azad, “Edge computing in IoT for smart healthcare,” J. Ambient Intell. Smart Environ., pp. 1–30, Jun. 2024, doi: 10.3233/AIS-230009.

Lilhore, U.K., Simaiya, S., Sharma, Y.K. et al. Cloud-edge hybrid deep learning framework for scalable IoT resource optimization. J Cloud Comp 14, 5 (2025), doi: 10.1186/s13677-025-00729-w.

A. Elhanashi, P. Dini, S. Saponara, and Q. Zheng, “TeleStroke: real-time stroke detection with federated learning and YOLOv8 on edge devices,” J. Real. Time. Image Process., vol. 21, no. 4, p. 121, Aug. 2024, doi: 10.1007/s11554-024-01500-1.

A. Brecko, E. Kajati, J. Koziorek, and I. Zolotova, “Federated Learning for Edge Computing: A Survey,” Applied Sciences, vol. 12, no. 18, p. 9124, Sep. 2022, doi: 10.3390/app12189124.

Arefin, T. and Azad, A. (2024) Design and Implementation of an IoT Based Remote Health Monitoring System. Journal of Computer and Communications, 12, 37-52. doi: 10.4236/jcc.2024.1211003.

R. Bhuvanya, T. Kujani, and K. Sivakumar, "Fusing Attention and Convolution: A Hybrid Model for Brain Stroke Prediction," EAI Endorsed Transactions on Scalable Information Systems, vol. 12, no. 1, Dec. 2024, doi: 10.4108/eetsis.7022.

A. M. A. Rahim, A. Sunyoto, and M. R. Arief, “Stroke Prediction Using Machine Learning Method with Extreme Gradient Boosting Algorithm,” MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer, vol. 21, no. 3, pp. 595–606, Jul. 2022, doi: 10.30812/matrik.v21i3.1666.

K. Moulaei, L. Afshari, R. Moulaei, B. Sabet, S. M. Mousavi, and M. R. Afrash, “Explainable artificial intelligence for stroke prediction through comparison of deep learning and machine learning models,” Sci. Rep., vol. 14, no. 1, p. 31392, Dec. 2024, doi: 10.1038/s41598-024-82931-5.

A. M. Mohamed, H. M. Amer, A. H. Rabie, A. I. Saleh, and M.-E. A. Abo-Elsoud, "Real-time monitoring system for early stroke detection based on fog computing and enhanced deep learning techniques," Sci. Rep., vol. 15, Art. no. 44671, Dec. 2025, doi: 10.1038/s41598-025-28513-5

H. Yuan et al., "Explainable machine learning for predicting neurological outcome in hemorrhagic and ischemic stroke patients in critical care," Front. Neurol., vol. 15, Art. no. 1385013, Jun. 2024, doi: 10.3389/fneur.2024.1385013.

J. Liu et al., “Edge-Cloud Collaborative Computing on Distributed Intelligence and Model Optimization: A Survey,” IEEE Communications Surveys & Tutorials, vol. 28, pp. 5049–5080, 2026, doi: 10.1109/COMST.2026.3669216.

A. Mashmool et al., “Edge Computing in Healthcare Using Machine Learning: A Systematic Literature Review,” WIREs Data Mining and Knowledge Discovery, vol. 16, no. 1, Mar. 2026, doi: 10.1002/widm.70069

fedesoriano, “Stroke Prediction Dataset.” https://www.kaggle.com/datasets/fedesoriano/stroke-prediction-dataset

I. M. Suartana, R. E. Putra, and Y. Ayuningtyas, “Implementation of particle swarm optimation-support vector machine with SMOTE for stroke classification,” 2024, p. 060027. doi: 10.1063/5.0210384.

A. E. Setiawan, S. Rustad, A. Syukur, M. A. Soeleman, M. Akrom, and A. W. Setiawan, “SMOTE-ENN Resampling to Optimize Diabetes Prediction in Imbalanced Data,” Ingénierie des systèmes d information, vol. 30, no. 5, pp. 1163–1176, May 2025, doi: 10.18280/isi.300505.

M. Qadri et al., "Edge Intelligence and Internet of Things in Healthcare: A Survey," IEEE Access, vol. 9, pp. 45–65, 2021, doi: 10.1109/ACCESS.2020.3045003.

Y. Zhuang, J. Zhang, J. Xu, D. Cao, and K. He, “A Cloud-Edge Collaborative System Based on the Framework of Multi-Device Semantic Interoperability in ICU,” Tsinghua Sci. Technol., vol. 31, no. 2, pp. 1216–1232, Apr. 2026, doi: 10.26599/TST.2024.9010245.

M. Safaei Yaraziz, N. Sohrabi Safa, and M. A. Azad, "Edge Computing in IoT for Smart Healthcare," Journal of Ambient Intelligence and Smart Environments, pp. 1–30, 2024, doi: 10.3233/AIS-230009.

S. Abdulmalek, A. Nasir, and W. A. Jabbar, "LoRaWAN-based Hybrid Internet of Wearable Things System Implementation for Smart Healthcare," Internet of Things, vol. 25, p. 101124, Feb. 2024, doi: 10.1016/j.iot.2024.101124.

K.-Y. Huang et al., “Enhancing healthcare AI stability with edge computing and machine learning for extubation prediction,” Sci. Rep., vol. 15, no. 1, p. 17858, May 2025, doi: 10.1038/s41598-025-02317-z.

M. Alberto, N. Filippo, A.N. Valerio, M. Serena, M. Sabato, C. Antonella, "Towards real-world clinical data standardization: A modular FHIR-driven transformation pipeline to enhance semantic interoperability in healthcare", Computers in Biology and Medicine, vol. 187, p.109745, Feb. 2025, doi: 10.1016/j.compbiomed.2025.109745.

T. Ha, T. K. Dang, T. T. Dang, T. A. Truong, and M. T. Nguyen, “Differential Privacy in Deep Learning: An Overview,” in 2019 International Conference on Advanced Computing and Applications (ACOMP), IEEE, Nov. 2019, pp. 97–102. doi: 10.1109/ACOMP.2019.00022.

M. Sung, D. Cha, and Y. R. Park, "Local differential privacy in the medical domain to protect sensitive information: algorithm development and real-world validation," JMIR Med. Inform., vol. 9, no. 11, Art. no. e26914, Nov. 2021, doi: 10.2196/26914.

S. S. Matta and M. Bolli, “Federated Learning For Privacy-Preserving Healthcare Data Sharing: Enabling Global Ai Collaboration,” American Journal of Scholarly Research and Innovation, vol. 04, no. 01, pp. 320–351, Jan. 2025, doi: 10.63125/jga18304.

Md. W. Rahman et al., “Privacy-Preserving Federated IoT Architecture for Early Stroke Risk Prediction,” Electronics (Basel)., vol. 15, no. 1, p. 32, Dec. 2025, doi: 10.3390/electronics15010032.

J. Waring, C. Lindvall, and R. Umeton, “Automated machine learning: Review of the state-of-the-art and opportunities for healthcare”, Artificial Intelligent in Medicine., Volume 104, 2020 doi: 10.1016/j.artmed.2020.101822.

Akinwumi PO, Ojo S, Nathaniel TI, Wanliss J, Karunwi O and Sulaiman M (2025) Evaluating machine learning models for stroke prediction based on clinical variables. Front. Neurol. 16:1668420. doi: 10.3389/fneur.2025.1668420

M. S. Singh, K. Thongam, K. Kumar, and P. Choudhary, "HMLA: A hybrid machine learning approach for enhancing stroke prediction models with missing data imputation techniques," Scientific Reports, vol. 16, no. 1, p. 673, Dec. 2025, doi: 10.1038/s41598-025-30203-1.

Y. Yan et al., “An Oversampling-Enhanced Multi-Class Imbalanced Classification Framework for Patient Health Status Prediction Using Patient-Reported Outcomes,” IEEE Access, vol. 13, pp. 175479–175494, 2025, doi: 10.1109/ACCESS.2025.3617316.

S. Kapoor and A. Narayanan, "Leakage and the reproducibility crisis in machine-learning-based science," Patterns, vol. 4, no. 9, Art. no. 100804, Sep. 2023, doi: 10.1016/j.patter.2023.100804.

F. Zhang et al., “Recent methodological advances in federated learning for healthcare,” Patterns, vol. 5, no. 6, p. 101006, Jun. 2024, doi: 10.1016/j.patter.2024.101006.

L. Breiman, “Random Forests,” Machine Learning, vol. 45, no. 1, pp. 5–32, Oct. 2001, doi: 10.1023/A:1010933404324.

P. Chakraborty, A. Bandyopadhyay, P. P. Sahu, A. Burman, S. Mallik, N. Alsubaie, M. Abbas, M. S. Alqahtani, and B. O. Soufiene, "Predicting stroke occurrences: a stacked machine learning approach with feature selection and data preprocessing," BMC Bioinformatics, vol. 25, p. 329, Oct. 2024, doi: 10.1186/s12859-024-05866-8.

F. G. Saleh Velez, R. Arevalo-Lorido, C. B. Pinheiro, and S. Prabhakaran, "Safer Stroke-Dx Instrument: Identifying Stroke Misdiagnosis in the Emergency Department," Circulation: Cardiovascular Quality and Outcomes, vol. 14, no. 10, p. e007758, Oct. 2021, doi: 10.1161/CIRCOUTCOMES.120.007758.

A. Tharwat, “Classification assessment methods,” Applied Computing and Informatics, vol. 17, no. 1, pp. 168–192, Jan. 2021, doi: 10.1016/j.aci.2018.08.003.

C. Ieracitano, N. Mammone, A. Bramanti, A. Hussain, and F. C. Morabito, “A Convolutional Neural Network approach for classification of dementia stages based on 2D-spectral representation of EEG recordings,” Neurocomputing, vol. 323, pp. 96–107, 2019, doi: 10.1016/j.neucom.2018.09.071.