A Trustworthy Healthcare Management Framework Using Amalgamation of AI and Blockchain Network

Abstract

Over the last few decades, the healthcare industry has continuously grown, with hundreds of thousands of patients obtaining treatment remotely using smart devices. Data security becomes a prime concern with such a massive increase in the number of patients. Numerous attacks on healthcare data have recently been identified that can put the patient’s identity at stake. For example, the private data of millions of patients have been published online, posing a severe risk to patients’ data privacy. However, with the advent of Industry 4.0, medical practitioners can digitally assess the patient’s condition and administer prompt prescriptions. However, wearable devices are also vulnerable to numerous security threats, such as session hijacking, data manipulation, and spoofing attacks. Attackers can tamper with the patient’s wearable device and relays the tampered data to the concerned doctor. This can put the patient’s life at high risk. Since blockchain is a transparent and immutable decentralized system, it can be utilized for securely storing patient’s wearable data. Artificial Intelligence (AI), on the other hand, utilizes different machine learning techniques to classify malicious data from an oncoming stream of patient’s wearable data. An amalgamation of these two technologies would make the possibility of tampering the patient’s data extremely difficult. To mitigate the aforementioned issues, this paper proposes a blockchain and AI-envisioned secure and trusted framework (HEART). Here, Long-Short Term Model (LSTM) is used to classify wearable devices as malicious or non-malicious. Then, we design a smart contract that allows only of those patients’ data having a wearable device to be classified as non-malicious to the public blockchain network. This information is then accessible to all involved in the patient’s care. We then evaluate the HEART’s performance considering various evaluation metrics such as accuracy, recall, precision, scalability, and network latency. On the training and testing sets, the model achieves accuracies of 93% and 92.92%, respectively.