Next-Generation Healthcare Management: AI and Blockchain Integration - Ghormode, Girish; Chaturvedi, Soni A.; Khurshid, A. A.; Rothe, J.P. (Journal of Neonatal Surgery, 2025)
Many distant patients now use smart wearable devices on their bodies to ensure dependable therapy; as a result, the healthcare sector is now far more effective; however, it is impacted by data security breaches, which were a major worry because of the astounding increase in patient volume. The privacy of medical records is at risk when hackers intercept data over the channel, alter the data, and interfere with the system. Approaches to diagnosis and treatment have improved since the advent of the 40 medical industries to provide precise and prompt therapy. Medical professionals or specialists consult digital data on the patient’s condition; however, frequent vulnerable assaults against wearable technology include spoofing, manipulation, and hijacking. Providing the concerned medical professional with tampered data could endanger the patient’s life. Information from patient’s wearable devices is stored on blockchain because of its immutable, decentralized network transparency and security. Artificial intelligence can be used to detect the legitimacy of wearable device data entering the blockchain by utilizing a machine-learned classifier. This study presents a highly secure and reliable framework based on air and blockchain technology to remove fraudulent samples and permit legitimate information to circulate throughout the network for patients to follow and medical professionals to evaluate. By picking important characteristics among 43 attributes, the dataset samples from the imbalanced west hems 2020 dataset that are being evaluated are processed effectively. The lower-class attack samples were expanded to balance the dataset; sums were utilized to normalize and decrease the dimension by SVM. And classification of the experiments revealed that the malicious samples could be differentiated from the normal samples with an accuracy of over 98%, outperforming other recent rival studies.