Blockchain and ML-Based Framework for Diabetes Assessment of Patients in Telesurgery System

With the advent of innovative technologies, smart healthcare services such as telemedicine and telesurgery adopted across the globe due to their prevalent benefits which helps to perform the surgery in an efficient and reliable manner. However, before the surgeries can be performed on the patient, the patient's health is to be monitored to declare the patient fit for the surgery. Therefore, we have proposed a blockchain-based diabetes detection scheme using machine learning. Our proposed framework leverages blockchain technology to safeguard sensitive data against potential malware threats. We have employed the Interplanetary File System (IPFS) protocol that improves the cost-efficiency of the proposed framework for reliable patient health monitoring. Moreover, we have implemented a random forest (RF) model to predict diabetes in a patient with an improved accuracy when compared to the other machine learning models. Finally, to assess the performance of the proposed framework, we have compared the performance of the proposed framework using RF based on various factors like accuracy, precision, recall, log-loss, and F1-score.