Exploring Applied Nonlinear Analysis and Machine Learning in The Evolution of Blockchain Technology

Blockchain technology is developing quickly, hence its application in data storage for healthcare presents a good way to increase data security and integrity. Blockchain's distributed nature and unchangeable ledger's character help to guard confidential medical records. Still, ensuring resilience and optimizing performance depend on the incorporation of innovative analytical methods. For current blockchain solutions for storing of healthcare data, efficiency of data retrieval and transaction security creates challenges. Big datasets are challenging for conventional methods, which also provide consistent access control. This work explores potential solutions for these issues by means of blockchain technology and Support Vector Machines (SVM) integration. Nonlinear analysis helps to improve blockchain system data classification and retrieval. The proposed method consists in preprocessing healthcare data, training an SVM model to recognize and anticipate access patterns, and embedding these predictions into blockchain transactions to maximize data storage and retrieval operations. Blockchain technology helped the SVM considerably increase data retrieval security and efficiency. Numerical testing reveal a 20% increase in retrieval speed and a 15% decrease in undesired access attempts, compared to standard methods. With 92% of SVM-based classification accuracy, access control systems become far more effective.