A Blockchain-Based Hybrid SVMLR Approach for IoT-Healthcare

There must have been a lot of interest in blockchain technology due to its potential applications in cognitive science, data monitoring, finance, computer security, IoT, and food chemistry. Using different bright particles, the healthcare architecture’s usage of multimedia further enables the processing, sending, and storing of patient data via the Internet in various formats, including text, voice, and images. But handling vast volumes of data, including individual results and photos, requires more work from people and raises security concerns. However, there are a range of threats associated with IoT devices that can arise from different adversaries. To prevent these issues, the best way to preserve the security and confidentiality of control systems in real time is blockchain technology. This should offer blockchain-based security architecture for the healthcare industry, ensuring that any changes or adjustments to data and any security breaches involving medications are documented throughout the blockchain network. This paper uses the newly designed hybrid method of support vector machine (SVM) with logistic regression (LR) to allocate healthcare resources more effectively. IoT technology helps solve these issues by enhancing patient care and reducing expenses. The outcomes confirmed that the model attained a high level of accuracy of 94.62%. We recommend the proposed blockchain-based hybrid SVM + LR model in IoT healthcare systems, which gives high overall efficiency predictive results. To assess the effectiveness of the suggested approach, MATLAB 2013A is utilized to create a machine learning algorithm.