Enhanced Medical Records Protection Using Quantum-Inspired Feature Optimization Based on AI-Driven Threat Detection with Tamper-Proof Blockchain Security
Keywords:
Artificial intelligence, Blockchain security, Distributed healthcare systems, Electronic medical records, Hybrid cryptography, Intrusion detection, Privacy preservationAbstract
The exponential growth of Electronic Medical Records (EMRs) has changed the way healthcare services are provided today by making it possible to monitor patients, diagnose illnesses, and make clinical decisions on a more effective basis than ever before. Unfortunately, the increased sharing of sensitive healthcare data across disparate environments has heightened concerns regarding confidentiality, integrity, unauthorized access, and cyberattacks. To help address these issues, an Intelligent Blockchain-Based Medical Records Protection Framework (IBMRPF) combines state-of-the-art feature optimization with deep analytics for detecting cyber threats and the inclusion of secure (tamper-proof) blockchain technology. The first phase of operation includes the acquisition and preprocessing of healthcare data using the MIMIC-III Clinical Database, Heart Disease UCI Dataset, and Diabetes 130-US Hospitals Dataset, which was accomplished using adaptive min-max normalization and data cleaning techniques. In addition, the new Quantum-Inspired Binary Enhanced Sparrow Search Optimization (QIBESSO) algorithm was developed to identify security-sensitive attributes related to healthcare and optimize the access-control features associated with them. The second phase of operation is centered on obtaining the security of the data through the use of a hybrid cryptographic protection layer constructed with Elliptic Curve Cryptography (ECC). This will allow for lightweight encryption and ensure fine-grained access control to the data provided to authorized entities within the healthcare industry. Finally, the third phase of operation is composed of a consortium blockchain that utilizes an Adaptive Quantum Swarm Consensus Algorithm (AQSCA) in order to provide immutable records of all transactions, provide a decentralized method for validating transactions, provide a secure mechanism for sharing data, and to protect against tampering of records. According to the findings of the validation study, the IBMRPF solution had a detection rate accuracy of 97.12%, precision of 97.86%, recall of 96.74%, and an F12 or F1 score of 95.80%, with a reduction in transaction validation time of 31.5%, encryption overhead of 28.7%, and computational cost of 26.9%. In addition, the throughput increased by 34.2%, and there was a 41.8% reduction in the number of unauthorized access events when compared to existing methods (Proof of Work and Proof of Authority) in healthcare security applications. By offering a scalable, automated, and private method for evolving healthcare ecosystems through secure management of electronic medical records and the sharing of reliable health information among distributed clinical environments.
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