An Efficient and Privacy-Preserving Scheme for Disease Prediction in Modern Healthcare Systems

Abstract

With the Internet of Things (IoT), mobile healthcare applications can now offer a variety of dimensionalities and online services. Disease Prediction Systems (DPS) increase the speed and accuracy of diagnosis, improving the quality of healthcare services. However, privacy is garnering an increasing amount of attention these days, especially concerning personal healthcare data, which are sensitive. There are a variety of prevailing privacy preservation techniques for disease prediction that are rendered. Nonetheless, there is a chance of medical users being affected by numerous disparate diseases. Therefore, it is vital to consider multi-label instances, which might decrease the accuracy. Thus, this paper proposes an efficient privacy-preserving (PP) scheme for patient healthcare data collected from IoT devices aimed at disease prediction in the modern Health Care System (HCS). The proposed system utilizes the Log of Round value-based Elliptic Curve Cryptography (LR-ECC) to enhance the security level during data transfer after the initial authentication phase. The authorized healthcare staff can securely download the patient data on the hospital side. Utilizing the Herding Genetic Algorithm-based Deep Learning Neural Network (EHGA-DLNN) can test these data with the trained system to predict the diseases. The experimental results demonstrate that the proposed approach improves prediction accuracy, privacy, and security compared to the existing methods.

Security and Privacy of Technologies in Health Information Systems: A Systematic Literature Review

“…Simple and efficient access control mechanism based on the agents' functionalities, Provides effective and secure e-health security services [40] Log of round value-based elliptic curve cryptography (LR-ECC) Herding genetic algorithm-based deep learning neural network (EHGA-DLNN)…”

“…Ref. [40] proposes a secure method for preserving privacy in healthcare data, specifically for disease prediction in modern healthcare systems. This system uses cryptography during data transfer and allows authorized healthcare staff to securely access patient data for disease prediction using a herding genetic algorithmbased deep learning neural network.…”

“…This approach effectively reduces the risk of data theft, cyberattacks, and other security threats [21,30,35,39,41,43]. Moreover, it ensures that only authorized individuals can access sensitive patient information, thereby protecting patient privacy and preventing unauthorized access to information that can be exploited for malicious purposes [23,[38][39][40]44]. Implementing secure access controls is crucial in preventing data breaches, which can be both costly and damaging to healthcare organizations.…”

An Insight Into Viable Machine Learning Models for Early Diagnosis of Cardiovascular Disease

“…With the rise of machine learning and deep learning, new techniques of medical diagnostic methods have been opened up to complement the expertise of physicians. Many studies such as Parkinson's disease [9,10], covid-19 [11,12] have already been made on a range of medical diagnostic topics with different levels of accuracy [3]. Ihe internet of things also playing a key role in health care systems [13].…”

Emerging Sensor Communication Network-Based AI/ML Driven Intelligent IoT

“…These are analysed from various angles to answer five main research questions. In [ 10 ] proposed system utilizes the Log of Round value-based Elliptic Curve Cryptography (LR-ECC) to enhance the security level during data transfer after the initial authentication phase. The authorized healthcare staff can securely download the patient’s data on the hospital side.…”