secure integration of electronic health data using advanced machine learning and blockchain technology

Introduction: data integration and privacy preservation in electronic health records (ehrs)remain major challenges. this study combines advanced machine learning and blockchain toimprove 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 securedata 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%); anovaindicated statistically significant differences (p<0.05). blockchain & interoperability (security& exchange): data-exchange success was 94%, combined privacy/security score 95%, with92% simulated attack prevention.conclusion: the combined approach shows promise for ehr integration and privacypreservation. validation on real multisite ehr data is recommended to confirmgeneralizability.