Secure Integration of Electronic Health Data Using Advanced Machine Learning and Blockchain Technology

Document Type : Original Article

Authors

1 Department of Health Information Technology, Sirjan School of Medical Sciences, Sirjan, Iran.

2 Department of Medicine, Sirjan School of Medical Sciences, Sirjan, Iran

3 Department of Occupational Health Engineering, Sirjan School of Medical Sciences, Sirjan, Iran.

Abstract

Introduction: Data integration and privacy preservation in electronic health records (EHRs)
remain major challenges. This study combines advanced machine learning and blockchain to
improve integration and security.
Methods: Using a synthetic multicenter EHR dataset (patient records, visits, diagnoses,
medications, observations, procedures), we evaluated an Irregular Fuzzy Cellular Automata
(IFCA) model—which incorporates fuzzy-logic rules—against XGBoost and LightGBM.
Preprocessing included complete anonymization and 98.5% missing-value imputation.
Machine learning addressed data integration, inconsistency resolution, and classification;
HL7-FHIR–like formats and a Hyperledger Fabric consortium blockchain evaluated secure
data exchange and access control. Analyses used Python 3.10 and R 4.2.
Results: Machine learning (data integrity & classification): IFCA achieved 92% accuracy
(F1=0.90, AUC-ROC=0.92), outperforming XGBoost (89%) and LightGBM (90%); ANOVA
indicated statistically significant differences (P<0.05). Blockchain & interoperability (security
& exchange): data-exchange success was 94%, combined privacy/security score 95%, with
92% simulated attack prevention.
Conclusion: The combined approach shows promise for EHR integration and privacy
preservation. Validation on real multisite EHR data is recommended to confirm
generalizability.

Highlights

Mostafa Kashani (Google Scholar)

Asma Zare (Google Scholar)

Keywords

Main Subjects

References

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Health Management & Information Science
Volume 12, Issue 4
October 2025
Pages 274-280

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