Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal International Journal of Electrical and Computer Engineering
Waqas, Ali
Unknown Affiliation
Computer Science & IT Electrical & Electronics Engineering

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Securing healthcare data and optimizing digital marketing through machine learning: the CAML-EHDS framework Abderrahmane, Fathi; Kawtar, Mouyassir; Waqas, Ali; Zahra, Fandi Fatima; Ali, Kartit
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i6.pp5728-5745

Abstract

Current healthcare data systems face major challenges in preventing unauthorized access, ensuring compliance with data privacy regulations, and enabling intelligent secondary use of patient information. To address these issues, we introduce cluster-based analysis with machine learning for enhanced healthcare data security (CAML-EHDS), a unified framework that combines homomorphic encryption, attribute-based elliptic curve cryptography (ECC), and semantic clustering with machine learning. CAML-EHDS improves upon existing models by offering fine-grained access control, adaptive threat detection, and data-driven insights while preserving privacy. Experimental results show that CAML-EHDS achieves up to 98% classification accuracy with low node count, and maintains 94% accuracy even at high node distribution levels, while ensuring encryption time under 24 seconds and acceptable data loss below 29%. Moreover, in comparative analysis with state-of-the-art models (support vector machine (SVM), random forest (RF), and decision tree (DT)), CAML-EHDS outperforms all in key metrics with an accuracy of 0.96. These results demonstrate CAML-EHDS’s potential for real-world deployment in secure, scalable, and intelligent healthcare environments, including privacy-aware digital marketing integration.
Co-Authors Abderrahmane, Fathi Ali, Kartit Kawtar, Mouyassir Zahra, Fandi Fatima