Blockchain-Based Secure Healthcare Application for Diabetic-Cardio Disease Prediction in Fog Computing

Shynu, P G; Menon, Varun G.; Kumar, Randhir; Kadry, Seifedine; Nam, Yunyoung

doi:10.1109/access.2021.3065440

Cited by 85 publications

(62 citation statements)

References 42 publications

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“…Figure 7 concludes that the proposed BBPM algorithm quickly responds to any user request compared with centralized storage and the existing BSF-EHR algorithm. Figure 8 compares the blockchain hash generation time of different algorithms using blockchain in healthcare, specifically Shynu et al [58], the BSF-EHR algorithm of Abunadi et al [57], and the proposed BBPM. is lightweight hash generation algorithm takes less time for hash generation than others.…”

Section: Resultsmentioning

confidence: 99%

Blockchain and Business Process Management in Health Care, Especially for COVID-19 Cases

Abunadi

Kumar

2021

Security and Communication Networks

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From this global health disaster, the health profession is searching for a new technology to monitor and mitigate COVID-19 infections. Accurate and reliable data are needed to surveil and prevent the diffusion of the coronavirus. However, there is a lack of consistent data on existing technologies. Various entities—for example, medical labs and public hospitals—could provide data on patients with coronavirus infection. Yet, this information might not be precise since it is not supervised and recorded correctly. This paper proposes a Blockchain and Business Process Management (BBPM) system in healthcare to solve these problems. This system could be crucial in tracing coronavirus diffusion and discovering more dangerous patients and is extremely proficient at controlling the disease. BBPM can be utilized like a digital database that includes information concurrently utilized and distributed inside a wide, decentralized, and openly accessible network. Usage of blockchains in the medical sector is anticipated to revolutionize the industry in many regions, primarily because centralization in current health technologies inhibits information sharing and causes a lack of confidentiality. Furthermore, BBPM could help supervise the diffusion of coronavirus infection worldwide by utilizing blockchain networks on the mobile devices of individuals. Protecting patient information is one of the critical strengths of BBPM. Participating BBPM nodes can be governments, hospitals, testing labs, or patients. In addition, the digital ledger has a few documents, including patient reports, consequences, the condition of treatment, and a summary of discharge. The BBPM system is categorized into four processes. In the first process, the patient is analyzed and diagnosed by a testing lab to detect any early signs of COVID-19 infection. A sample of the patients was taken. If it is positive, the second process begins. In the second process, the patient is isolated and begins treatment for a minimum of 14 days. If the patient’s health condition is improving, the third process begins. In the third process, the patients were retested for COVID-19. If the patient sample is negative, the patient is discharged, and an outline is created. During discharge, the patient pays the hospital for treatment. However, if the patient sample is positive, the isolation period is continued for another 14 days. In the last process, the details of total COVID-19 are confirmed, recovered, and death cases are conveyed to the government by the testing labs and hospitals. Patient records are stored for upcoming usage; their confidentiality is maintained when distributed on a larger scale. BBPM can guarantee the security and accuracy of patients’ recorded data.

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Section: Resultsmentioning

confidence: 99%

Blockchain and Business Process Management in Health Care, Especially for COVID-19 Cases

Abunadi

Kumar

2021

Security and Communication Networks

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“…In a study [ 28 ], they proposed a blockchain-based health program for predicting diabetes in fog computing technology. In their method, patient health information is collected from fog nodes and stored in a blockchain.…”

Section: Related Workmentioning

confidence: 99%

Patient privacy in smart cities by blockchain technology and feature selection with Harris Hawks Optimization (HHO) algorithm and machine learning

Al-Safi

Munilla

Rahebi

2022

Multimed Tools Appl

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A medical center in the smart cities of the future needs data security and confidentiality to treat patients accurately. One mechanism for sending medical data is to send information to other medical centers without preserving confidentiality. This method is not impressive because in treating people, the privacy of medical information is a principle. In the proposed framework, the opinion of experts from other medical centers for the treatment of patients is received and consider the best therapy. The proposed method has two layers. In the first layer, data transmission uses blockchain. In the second layer, blocks related to patients’ records analyze by machine learning methods. Patient records place in a block of the blockchain. Block of patient sends to other medical centers. Each treatment center can recommend the proposed type of treatment and blockchain attachment and send it to all nodes and treatment centers. Each medical center receiving data of the patients, then predicts the treatment using data mining methods. Sending medical data between medical centers with blockchain and maintaining confidentiality is one of the innovations of this article. The proposed method is a binary version of the HHO algorithm for feature selection. Another innovation of this research is the use of majority voting learning in diagnosing the type of disease in medical centers. Implementation of the proposed system shows that the blockchain preserves data confidentiality of about 100%. The reliability and reliability of the proposed framework are much higher than the centralized method. The result shows that the accuracy, sensitivity, and precision of the proposed method for diagnosing heart disease are 92.75%, 92.15%, and 95.69%, respectively. The proposed method has a lower error in diagnosing heart disease from ANN, SVM, DT, RF, AdaBoost, and BN.

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“…For the purpose design a privacy and security healthcare IoT system to diagnostic accuracy diabetic and cardio disease, Shynu et al [ 46 ] proposed an integrated solution between healthcare IoT application, blockchain and fog computing technologies. Specifically, sensors will collect patient data and storage on the blockchain.…”

Section: Survey Of Recent Iot Healthcare Applicationsmentioning

confidence: 99%

Smart healthcare IoT applications based on fog computing: architecture, applications and challenges

Quy

Nguyen

Anh

et al. 2021

Complex Intell. Syst.

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The history of human development has proven that medical and healthcare applications for humanity always are the main driving force behind the development of science and technology. The advent of Cloud technology for the first time allows providing systems infrastructure as a service, platform as a service and software as a service. Cloud technology has dominated healthcare information systems for decades now. However, one limitation of cloud-based applications is the high service response time. In some emergency scenarios, the control and monitoring of patient status, decision-making with related resources are limited such as hospital, ambulance, doctor, medical conditions in seconds and has a direct impact on the life of patients. To solve these challenges, optimal computing technologies have been proposed such as cloud computing, edge computing, and fog computing technologies. In this article, we make a comparison between computing technologies. Then, we present a common architectural framework based on fog computing for Internet of Health Things (Fog-IoHT) applications. Besides, we also indicate possible applications and challenges in integrating fog computing into IoT Healthcare applications. The analysis results indicated that there is huge potential for IoHT applications based on fog computing. We hope, this study will be an important guide for the future development of fog-based Healthcare IoT applications.

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Blockchain-Based Secure Healthcare Application for Diabetic-Cardio Disease Prediction in Fog Computing

Cited by 85 publications

References 42 publications

Blockchain and Business Process Management in Health Care, Especially for COVID-19 Cases

Blockchain and Business Process Management in Health Care, Especially for COVID-19 Cases

Patient privacy in smart cities by blockchain technology and feature selection with Harris Hawks Optimization (HHO) algorithm and machine learning

Smart healthcare IoT applications based on fog computing: architecture, applications and challenges

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