Have a personal or library account? Click to login
A Hybrid Deep Learning Algorithm Based Prediction Model for Sustainable Healthcare System Cover

A Hybrid Deep Learning Algorithm Based Prediction Model for Sustainable Healthcare System

Open Access
|Jun 2025

References

  1. J.P. Li, et al., “Heart Disease Identification Method Using Machine Learning Classification in E-Healthcare,” IEEE Access, vol. 8, 2020, pp. 107562–107582.
  2. A.O. Afolabi and P. Toivanen, “Integration of Recommendation Systems into Connected Health for Effective Management of Chronic Diseases,” IEEE Access, vol. 7, 2019, pp. 49201–49211.
  3. S.J. Pasha and E.S. Mohamed, “Novel Feature Reduction (NFR) Model With Machine Learning and Data Mining Algorithms for Effective Disease Risk Prediction,” IEEE Access, vol. 8, 2020, pp. 184087–184108.
  4. H. Yu and Z. Zhou, “Optimization of IoT-Based Artificial Intelligence Assisted Telemedicine Health Analysis System” IEEE Access, vol. 9, 2021, pp. 85034–85048.
  5. G.S. Bhat, et al., “Machine Learning-based Asthma Risk Prediction Using IoT and Smartphone Applications,” IEEE Access, vol. 8, 2021, pp. 118708–118715.
  6. H. Wang, et al., “Integrating Co-Clustering and Interpretable Machine Learning for the Prediction of Intravenous Immunoglobulin Resistance in Kawasaki Disease,” IEEE Access, vol. 8, 2020, pp. 97064–97071.
  7. E. Longato, et al., “A Deep Learning Approach to Predict Diabetes’ Cardiovascular Complications from Administrative Claims” IEEE Journal of Biomedical and Health Informatics, vol. 25. 2021, pp. 3608–3617.
  8. P. Zhang, X. Huang, and M. Li, “Disease Prediction and Early Intervention System Based on Symptom Similarity Analysis,” vol. 8, 2019, pp. 176484–176494.
  9. Q. Wang, et al., “Diagnosis of Chronic Obstructive Pulmonary Disease Based on Transfer Learning,” IEEE Access, vol. 8, 2020, pp. 47370–47383.
  10. X. Xue, “A Study on an Application System for the Sustainable Development of Smart Healthcare in China,” IEEE Access, vol. 9, 2021, pp. 111960– 111974.
  11. N. Sedaghat, et al., “Combining Supervised and Unsupervised Learning for Improved miRNA Target Prediction,” IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 15, 2018, pp. 1594–1604.
  12. J. Xie and Q. Wang, “Benchmarking Machine Learning Algorithms on Blood Glucose Prediction for Type I Diabetes in Comparison with Classical Time-Series Models,” IEEE Transactions on Biomedical Engineering, vol. 67, 2020, pp. 3101– 3124.
  13. R. Alanazi, “Identification and Prediction of Chronic Diseases Using Machine Learning Approach,” Journal of Healthcare Engineering, 2022, pp. 1–9.
  14. G.M.I.S. Bourouis, et al., “Comparative Analysis for Prediction of -Kidney Disease Using Intelligent Machine Learning Methods,” Computational and Mathematical Methods in Medicine, 2021, pp. 1–10.
  15. C. Peipei, et al., “Interpretable Clinical Prediction via Attention-Based Neural Network,” BMC Medical Informatics and Decision Making, vol. 20, no.131, 2020, pp. 1–9.
  16. R.G. Nadakinamani, et al., “Clinical Data Analysis for Prediction of Cardiovascular Disease Using Machine Learning Techniques,” Computational Intelligence and Neuroscience, 2022, pp. 1–13.
  17. P. Khan, et al., “Machine Learning and Deep Learning Approaches for Brain Disease Diagnosis: Principles and Recent Advances,” IEEE Access, vol. 9, 2021, pp. 37622–37655.
  18. T. Wang, Y. Tian, and R.G. Qiu (2020) “Long ShortTerm Memory Recurrent Neural Networks for Multiple Diseases Risk Prediction by Leveraging Longitudinal Medical Records,” IEEE Journal of Biomedical and Health Infomatics, vol. 24, pp. 2337–2346.
  19. P. Chittora, et al., “Prediction of Chronic Kidney Disease A Machine Learning Perspective,” IEEE Access, vol. 9, 2021, pp. 17312–17334.
  20. A. Rahim, et al., “An Integrated Machine Learning Framework for Effective Prediction of Cardiovascular Diseases,” IEEE Access, vol. 9, 2021, pp. 106575–106588.
  21. J. Luo and Y. Long, “NTSHMDA: Prediction of Human Microbe-Disease Association Based on Random Walk by Integrating Network Topological Similarity,” IEEE/ACM Transactions On Computational Biology And Bioinformatics, vol. 17, pp. 1341–1352.
  22. D. Chicco and G. Jurman, “Arterial Disease Computational Prediction and Health Record Feature Ranking Among Patients Diagnosed With Inflammatory Bowel Disease,” IEEE Access, vol. 9, 2021, pp. 78648–78658.
  23. P. Wu, et al., “An Effective Machine Learning Approach for Identifying Non-Severe and Severe Coronavirus Disease 2019 Patients in a Rural Chinese Population: The Wenzhou Retrospective Study,” IEEE Access, vol. 9, 2021, pp. 45486– 45503.
  24. J. Prince, F. Andreotti, and M. De Vos, “MultiSource Ensemble Learning for the Remote Prediction of Parkinson’s Disease in the Presence of Source-Wise Missing Data,” IEEE Transactions on Biomedical Engineering, vol. 66, 2019, pp. 1402– 1411.
  25. Y. Zhao, et al., “Prediction of Alzheimer’s Disease Progression with Multi-Information Generative Adversarial Network,” IEEE Journal of Biomedical and Health Informatics, vol. 25, 2020, pp. 711– 719.
  26. S. Nandy, “An Intelligent Heart Disease Prediction System Based on Swarm Artificial Neural Network,” Neural Computing and Applications, 2021, pp. 1–15.
DOI: https://doi.org/10.14313/jamris-2025-019 | Journal eISSN: 2080-2145 | Journal ISSN: 1897-8649
Language: English
Page range: 89 - 98
Submitted on: Sep 28, 2023
Accepted on: May 15, 2024
Published on: Jun 26, 2025
Published by: Łukasiewicz Research Network – Industrial Research Institute for Automation and Measurements PIAP
In partnership with: Paradigm Publishing Services
Publication frequency: 4 times per year

© 2025 K Tharageswari, N Mohana Sundaram, R Santhosh, published by Łukasiewicz Research Network – Industrial Research Institute for Automation and Measurements PIAP
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.