Blockchain Papers

Yi Xie, Lin Lu, Fei Gao, Shuangjiang He · 10 authors

No abstract is available for this record.

Ioannis Karamitsos, Maria Papadaki

COVID-19 is a pandemic outbreak for each country worldwide. Each government needs to monitor every citizen and the COVID-19 test becomes an essential evidence for people who are travelling. This gives rise to the necessity of disruptive technologies such as Blockchain. In this paper, we provide an overview of the Hyperledger and Ethereum platforms and present how healthcare organizations can control and monitor digital health test certificates with citizens or other stakeholders. We also present a smart contract structure and implementation for COVID-19 test certificates in both blockchain platforms.

Andy S. K. Cheng, Qiongyao Guan, Yan Su, Ping Zhou · 5 authors

This brief report aimed to describe a narrative review about the application of machine learning (ML) methods and Blockchain technology (BCT) in the healthcare field, and to illustrate the integration of these two technologies in cancer survivorship care. A total of six eligible papers were included in the narrative review. ML and BCT are two data-driven technologies, and there is rapidly growing interest in integrating them for clinical data management and analysis in healthcare. The findings of this report indicate that both technologies can integrate feasibly and effectively. In conclusion, this brief report provided the state-of-art evidence about the integration of the most promising technologies of ML and BCT in health field, and gave an example of how to apply these two most disruptive technologies in cancer survivorship care.

Ruonan Wang, Min Luo, Yihong Wen, Lianhai Wang · 6 authors

There has been increased interest in applying artificial intelligence (AI) in various settings to inform decision-making and facilitate predictive analytics. In recent times, there have also been attempts to utilize blockchain (a peer-to-peer distributed system) to facilitate AI applications, for example, in secure data sharing (for model training), preserving data privacy, and supporting trusted AI decision and decentralized AI. Hence, in this paper, we perform a comprehensive review of how blockchain can benefit AI from these four aspects. Our analysis of 27 English-language articles published between 2018 and 2021 identifies a number of research challenges and opportunities.

M. Supriya, Vijay Kumar Chattu

Artificial intelligence (AI) programs are applied to methods such as diagnostic procedures, treatment protocol development, patient monitoring, drug development, personalized medicine in healthcare, and outbreak predictions in global health, as in the case of the current COVID-19 pandemic. Machine learning (ML) is a field of AI that allows computers to learn and improve without being explicitly programmed. ML algorithms can also analyze large amounts of data called Big data through electronic health records for disease prevention and diagnosis. Wearable medical devices are used to continuously monitor an individual’s health status and store it in cloud computing. In the context of a newly published study, the potential benefits of sophisticated data analytics and machine learning are discussed in this review. We have conducted a literature search in all the popular databases such as Web of Science, Scopus, MEDLINE/PubMed and Google Scholar search engines. This paper describes the utilization of concepts underlying ML, big data, blockchain technology and their importance in medicine, healthcare, public health surveillance, case estimations in COVID-19 pandemic and other epidemics. The review also goes through the possible consequences and difficulties for medical practitioners and health technologists in designing futuristic models to improve the quality and well-being of human lives.

Sukhpal Singh Gill, Ricardo Vinuesa, Venki Balasubramanian, Soumya K. Ghosh

We are pleased to present a special issue that focuses on the software systems for managing the impact of the coronavirus disease 19 (COVID-19) pandemic. The COVID-19 pandemic has affected around 192 million people worldwide and has led to ˜4.13 million deaths as of July 22, 2021. Globally, most of the countries have implemented lockdowns to protect their citizens. However, lockdown over an extended period is unsustainable. Hence, it is widely believed that virus testing and tracking is the best approach to ease lockdown measures. There is a need for innovative software systems to manage the impact of the COVID-19 pandemic effectively in many areas such as healthcare system, transport systems, supply-chain system, educational system, government-service delivery, pharmaceutical companies, manufacturing, software industries, and multinational companies. For example, in healthcare, smart-software systems would be able to remotely measure a person's body temperature, heart and respiratory rates, identifying their movements (including sneezing, coughing, shivering, etc.) to identify whether a person is displaying symptoms of COVID-19 or not. An essential aspect associated with these technologies is data privacy, scalability, and quality of service (QoS) in terms of reliability, availability, security, latency, and energy which need to be considered throughout the development of the software systems. In countries like India, UK, Russia, Brazil, and USA, the system would also help to ensure that isolated communities have access to testing, delivered in a fast, accurate, and efficient manner. These software systems would help and support the assessment of public-health strategies and policies such as social distancing and assess further interventions to control the spread of the virus. Innovative software systems can increase stakeholder participation, as cost-effective assistance in the COVID-19 pandemic monitoring is of great interest to many countries. To manage the impact of this pandemic, there is a need to design and develop scalable, reliable, and energy-efficient sustainable software solutions for different COVID-19 scenarios. In consideration of the existing systems and their features, an Internet of Things (IoT)-based system suitable for COVID-19 or pandemic situations associated with other influenza viruses can be developed. Furthermore, these systems can be integrated with artificial-intelligence (AI) processes for effective data-collection, analysis, statistical visualization, sharing, and decision making. Moreover, these systems can be implemented using both simulations and real-time testbeds for COVID-19 operations (sanitization, medication, monitoring, thermal imaging, etc.) to test their performance in terms of scalability, reliability, availability, and energy efficiency. There is a need to use AI methods, such as reinforcement learning, deep learning, and genetic algorithms while developing IoT-based software systems to achieve self-learning, self-adaptation, and autonomous decision-making capabilities in order to improve efficiency of the systems. Meanwhile, a huge voluminous amount of complex data is generated from various sources including World Health Organization (WHO), social networking, edge devices, private and public hospitals, patients and academic institutes, which needs an effective big data analytics mechanism to manage this data proficiently. Furthermore, there is a need to study the impact of system configuration on workload processing at different cloud nodes while maintaining the QoS dynamically. The data are collected in databases, it is subsequently examined and monitored, and it is important to manage data consistency and integrity. In this context, we argue that it is essential to employ decentralized data-gathering approaches, maintaining the privacy of the population as a high priority. This special issue has received articles by researchers and practitioners from both academia and industry to develop innovative software systems for managing the impact of the COVID-19 pandemic. This special issue, therefore, aims to focus the attention of its readers to four research articles carefully selected after multiple rounds of peer-review. The brief contributions of these papers are discussed in the following section: The first paper entitled “An approach to forecast impact of COVID-19 using supervised machine learning model” by Mohan et al.1 proposes a hybrid model to predict the effect of COVID-19 using moving regressive, autoregressive, and ensemble learning model. This work uses two datasets from Worldometer and Ministry of Health & Family Welfare of India to conduct the countrywise predictions across the world and statewise predictions of India, respectively. The second paper entitled “NovidChain: Blockchain-based privacy-preserving platform for COVID-19 test/vaccine certificates” by Abid et al.2 includes various promising ideas such as maintains the immutability and data integrity using Blockchain technology, enhances the privacy by incorporating encryption for personal information and verifies the COVID-19 proof using W3C verifiable credentials standard immediately. The third paper entitled “Software System to Predict the Infection in COVID-19 Patients using Deep Learning and Web of Things” by Singh et al.3 generates synthetic data using various data augmentation techniques. Proposed system uses U-Net and WoT to segment the COVID Medseg and Radiopedia datasets in an autonomic manner. Experimental results show that the system gives better performance in terms of network latency, response time, and server latency. The fourth paper entitled “Advanced Data Integration in Banking, Financial, and Insurance Software in the Age of COVID-19” by Maiti et al.4 contributes to recognize the effect of the COVID-19 pandemic on the global Banking Financial Services and Insurance landscape. Further, a hype cycle has been developed to find out the important software technologies to handle real-world challenges related to corporate. We believe the work that has been approved in this special issue will assist readers of the journal and a broader research community to learn about the topics of software systems and impacts of COVID-19 pandemic, and inspire them to study more in this area. We would like to express our gratitude to the Editor-in-Chief (Prof. Rajkumar Buyya) and editorial board members for allowing us to bring out this special issue and guiding us throughout the process. We also want to express our gratitude to and further acknowledge the administrative staff, reviewers, and especially the authors for their contributions to the success of this issue.

Priti Tagde, Sandeep Tagde, Sandeep Tagde, Tanima Bhattacharya · 10 authors

Blockchain and artificial intelligence technologies are novel innovations in healthcare sector. Data on healthcare indices are collected from data published on Web of Sciences and other Google survey from various governing bodies. In this review, we focused on various aspects of blockchain and artificial intelligence and also discussed about integrating both technologies for making a significant difference in healthcare by promoting the implementation of a generalizable analytical technology that can be integrated into a more comprehensive risk management approach. This article has shown the various possibilities of creating reliable artificial intelligence models in e-Health using blockchain, which is an open network for the sharing and authorization of information. Healthcare professionals will have access to the blockchain to display the medical records of the patient, and AI uses a variety of proposed algorithms and decision-making capability, as well as large quantities of data. Thus, by integrating the latest advances of these technologies, the medical system will have improved service efficiency, reduced costs, and democratized healthcare. Blockchain enables the storage of cryptographic records, which AI needs.

Reyes-González Juan Pablo, Roberto Díaz-Peregrino, Victor SOTO-ULLOA, Isabel GALVAN-REMIGIO · 6 authors

In the last decades big data has facilitating and improving our daily duties in the medical research and clinical fields; the strategy to get to this point is understanding how to organize and analyze the data in order to accomplish the final goal that is improving healthcare system, in terms of cost and benefits, quality of life and outcome patient. The main objective of this review is to illustrate the state-of-art of big data in healthcare, its features and architecture. We also would like to demonstrate the different application and principal mechanisms of big data in the latest technologies known as blockchain and artificial intelligence, recognizing their benefits and limitations. Perhaps, medical education and digital anatomy are unexplored fields that might be profitable to investigate as we are proposing. The healthcare system can be revolutionized using these different technologies. Thus, we are explaining the basis of these systems focused to the medical arena in order to encourage medical doctors, nurses, biotechnologies and other healthcare professions to be involved and create a more efficient and efficacy system.

Sin Kit Lo, Yue Liu, Qinghua Lu, Chen Wang · 7 authors

Federated learning is an emerging privacy-preserving AI technique where clients (i.e., organisations or devices) train models locally and formulate a global model based on the local model updates without transferring local data externally. However, federated learning systems struggle to achieve trustworthiness and embody responsible AI principles. In particular, federated learning systems face accountability and fairness challenges due to multi-stakeholder involvement and heterogeneity in client data distribution. To enhance the accountability and fairness of federated learning systems, we present a blockchain-based trustworthy federated learning architecture. We first design a smart contract-based data-model provenance registry to enable accountability. Additionally, we propose a weighted fair data sampler algorithm to enhance fairness in training data. We evaluate the proposed approach using a COVID-19 X-ray detection use case. The evaluation results show that the approach is feasible to enable accountability and improve fairness. The proposed algorithm can achieve better performance than the default federated learning setting in terms of the model's generalisation and accuracy.

Yang Li, Biaoan Shan, Beiwei Li, Xiaoju Liu · 5 authors

The emergence of machine learning (ML) and blockchain (BC) technology has greatly enriched the functions and services of healthcare, giving birth to the new field of "smart healthcare." This study aims to review the application of ML and BC technology in the smart medical industry by Web of Science (WOS) using bibliometric visualization. Through our research, we identify the countries with the greatest output, the major research subjects, funding funds, and the research hotspots in this field. We also find out the key themes and future research areas in application of ML and BC technology in healthcare area. We reveal the different aspects of research under the two technologies and how they relate to each other around five themes.

Mohamed Yaseen Jabarulla, Heung-No Lee

The world is facing multiple healthcare challenges because of the emergence of the COVID-19 (coronavirus) pandemic. The pandemic has exposed the limitations of handling public healthcare emergencies using existing digital healthcare technologies. Thus, the COVID-19 situation has forced research institutes and countries to rethink healthcare delivery solutions to ensure continuity of services while people stay at home and practice social distancing. Recently, several researchers have focused on disruptive technologies, such as blockchain and artificial intelligence (AI), to improve the digital healthcare workflow during COVID-19. Blockchain could combat pandemics by enabling decentralized healthcare data sharing, protecting users' privacy, providing data empowerment, and ensuring reliable data management during outbreak tracking. In addition, AI provides intelligent computer-aided solutions by analyzing a patient's medical images and symptoms caused by coronavirus for efficient treatments, future outbreak prediction, and drug manufacturing. Integrating both blockchain and AI could transform the existing healthcare ecosystem by democratizing and optimizing clinical workflows. In this article, we begin with an overview of digital healthcare services and problems that have arisen during the COVID-19 pandemic. Next, we conceptually propose a decentralized, patient-centric healthcare framework based on blockchain and AI to mitigate COVID-19 challenges. Then, we explore the significant applications of integrated blockchain and AI technologies to augment existing public healthcare strategies for tackling COVID-19. Finally, we highlight the challenges and implications for future research within a patient-centric paradigm.

Basetty Mallikarjuna, Gulshan Shrivastava, Meenakshi Sharma

The healthcare technologies in COVID-19 pandemic had grown immensely in various domains. Blockchain technology is one such turnkey technology, which is transforming the data securely; to store electronic health records (EHRs), develop deep learning algorithms, access the data, process the data between physicians and patients to access the EHRs in the form of distributed ledgers. Blockchain technology is also made to supply the data in the cloud and contact the huge amount of healthcare data, which is difficult and complex to process. As the complexity in the analysis of data is increasing day by day, it has become essential to minimize the risk of data complexity. This paper supports deep neural network (DNN) analysis in healthcare and COVID-19 pandemic and gives the smart contract procedure, to identify the feature extracted data (FED) from the existing data. At the same time, the innovation will be useful to analyse future diseases. The proposed method also analyze the existing diseases which had been reported and it is extremely useful to guide physicians in providing appropriate treatment and save lives. To achieve this, the massive data is integrated using Python scripting language under various libraries to perform a wide range of medical and healthcare functions to infer knowledge that assists in the diagnosis of major diseases such as heart disease, blood cancer, gastric and COVID-19.

Francesco Sanmarchi, F Toscano, M Fattorini, Andrea Bucci · 5 authors

Modern healthcare management and clinical practice strongly rely on data and scientific evidence. Digital technologies, tools, and services are core components of Healthcare Management and scientific Research (HMR). Data interoperability, security, privacy, and ease of sharing represent fundamental conditions for guaranteeing quality HMR. Current data management solutions in HMR are mainly built on two technological infrastructures: cloud-based (CB) or distributed ledger systems (DLTs). DLTs offer alternative and reliable alternatives for the management and sharing of data in HMR. Their use can help increase confidence and trust in the integrity of data and the resulting evidence. 
\nThe aim of this paper is to shed light on CB and DLT solutions, emphasizing the potential role of innovative digital solutions based on DLTs in creating a data-driven transformation of HMR, and to describe relevant examples and practical uses of DLT-based solutions for patients, healthcare management, and research activities. 
\nDLTs in particular can be increasingly useful for patients to truly have control over their health, for healthcare policymakers to increase the quality of organizational processes, and for research funders, editors and publishers to increase the return on investment, and the reuse and reproducibility of research. 
\nIn conclusion, harnessing the potential of digital technologies is essential to transform healthcare management and research, by enhancing data quality, reliability, and trust.

Leila Fetjah, Kebira Azbeg, Ouail Ouchetto, Said Jai Andaloussi

With the rapid development in smart medical devices, Internet of things has a large applicability in healthcare sector. The current system is based on a centralized communication with cloud servers. However, this architecture increases security and privacy risks. This paper describes an architecture of a smart healthcare system for remote patient monitoring. To ensure security and privacy, the architecture uses the Blockchain technology. For data analysis, smart contracts and artificial intelligence are used. The architecture is divided into three layers: smart medical devices layer, fog layer and cloud layer. To validate the proposed approach, a scenario based on diabetes management system is described. The architecture is applied to provide remote diabetic patients monitoring. The system could suggest treatments, generate proactive predictions and predict future complications as well as alerting physicians in case of emergency.

European Society of Radiology (ESR), Elmar Kotter, Luis Martí‐Bonmatí, Adrian P. Brady · 5 authors

Blockchain can be thought of as a distributed database allowing tracing of the origin of data, and who has manipulated a given data set in the past. Medical applications of blockchain technology are emerging. Blockchain has many potential applications in medical imaging, typically making use of the tracking of radiological or clinical data. Clinical applications of blockchain technology include the documentation of the contribution of different "authors" including AI algorithms to multipart reports, the documentation of the use of AI algorithms towards the diagnosis, the possibility to enhance the accessibility of relevant information in electronic medical records, and a better control of users over their personal health records. Applications of blockchain in research include a better traceability of image data within clinical trials, a better traceability of the contributions of image and annotation data for the training of AI algorithms, thus enhancing privacy and fairness, and potentially make imaging data for AI available in larger quantities. Blockchain also allows for dynamic consenting and has the potential to empower patients and giving them a better control who has accessed their health data. There are also many potential applications of blockchain technology for administrative purposes, like keeping track of learning achievements or the surveillance of medical devices. This article gives a brief introduction in the basic technology and terminology of blockchain technology and concentrates on the potential applications of blockchain in medical imaging.

Jan Witowski, Jongmum Choi, Soomin Jeon, Doyun Kim · 9 authors

Current research on medical image processing relies heavily on the amount and quality of input data. Specifically, supervised machine learning methods require well-annotated datasets. A lack of annotation tools limits the potential to achieve high-volume processing and scaled systems with a proper reward mechanism. We developed MarkIt, a web-based tool, for collaborative annotation of medical imaging data with artificial intelligence and blockchain technologies. Our platform handles both Digital Imaging and Communications in Medicine (DICOM) and non-DICOM images, and allows users to annotate them for classification and object detection tasks in an efficient manner. MarkIt can accelerate the annotation process and keep track of user activities to calculate a fair reward. A proof-of-concept experiment was conducted with three fellowship-trained radiologists, each of whom annotated 1,000 chest X-ray studies for multi-label classification. We calculated the inter-rater agreement and estimated the value of the dataset to distribute the reward for annotators using a crypto currency. We hypothesize that MarkIt allows the typically arduous annotation task to become more efficient. In addition, MarkIt can serve as a platform to evaluate the value of data and trade the annotation results in a more scalable manner in the future. The platform is publicly available for testing on https://markit.mgh.harvard.edu.

Daniel Cabrera, Christopher P Nickson, Damian Roland, Elissa Hall · 5 authors

UNSTRUCTURED Current health professions education (HPE) institutions are based on an assembly-line hierarchical structure. The last decade has witnessed the advent of sophisticated networks allowing the exchange of information and educational assets. Blockchain provides an ideal data management framework that can support high-order applications such as learning systems and credentialing in an open and a distributed fashion. These system management characteristics enable the creation of a distributed autonomous organization of learning (DAOL). This new type of organization allows for the creation of decentralized adaptive competency curricula, simplification of credentialing and certification, leveling of information asymmetry among educational market stakeholders, assuring alignment with societal priorities, and supporting equity and transparency.

Tim Joda, Nikolaos Pandis

No abstract is available for this record.

Anjum Khurshid, Cole Holan, Cody Cowley, Jeremiah Alexander · 9 authors

OBJECTIVE: Healthcare systems suffer from a lack of interoperability that creates "data silos," causing patient linkage and data sharing problems. Blockchain technology's unique architecture provides individuals greater control over their information and may help address some of the problems related to health data. A multidisciplinary team designed and tested a blockchain application, MediLinker, as a patient-centric identity management system. METHODS: The study used simulated data of "avatars" representing different types of patients. Thirty study participants were enrolled to visit simulated clinics, and perform various activities using MediLinker. Evaluation was based on Bouras' criteria for patient-centric identity management and on the number of errors in entry and sharing of data by participants. RESULTS: Twenty-nine of the 30 participants completed all study activities. MediLinker fulfilled all of Bouras' criteria except for one which was not testable. A majority of data errors were due to user error, such as wrong formatting and misspellings. Generally, the number of errors decreased with time. Due to COVID-19, sprint 2 was completed using "virtual" clinic visits. The number of user errors were less in virtual visits than in personal visits. DISCUSSION: The evaluation of MediLinker provides some evidence of the potential of a patient-centric identity management system using blockchain technology. The results showed a working system where patients have greater control over their information and can also easily provide consent for use of their data. CONCLUSION: Blockchain applications for identity management hold great promise for use in healthcare but further research is needed before real-world adoption.

Satyabrata Aich, Nday Kabulo Sinai, Saurabh Kumar, Mohammed Al Ali · 7 authors

For decades artificial intelligence (AI) has been used for various applications in the healthcare industry. Machine learning and artificial intelligence algorithms allow us to diagnose and customize medical care and follow-up plans to get better results, and during the covid19 pandemic, it was found that AI models have been using to predict the Covid-19 symptoms, understanding how it spreads, speeding up research and treatment using medical data. However, it is very challenging to make a robust AI model and use it in a real-time and real-world environment since most organizations do not want to share their data with other third parties due to privacy concerns, furthermore, it is difficult to build a generalized prediction model because of the fragmented nature of the patient data across the healthcare system. To solve the above problems, this paper presents a solution based on blockchain and AI technologies. The blockchain will securely protect the data access and AI-based federated learning for building a robust model for global and real-time usage.

Xiangfeng Zhang, Yanmei Wang

Abstract In order to improve the intelligence of the medical system, this paper designs and implements a secure medical big data ecosystem on top of the Hadoop big data platform. It is designed against the background of the increasingly serious trend of the current security medical big data ecosystem. In order to improve the efficiency of traditional medical rehabilitation activities and enable patients to maximize their understanding of their treatment status, this paper designs a personalized health information system that allows patient users to understand their treatment and rehabilitation status anytime and anywhere, and all medical health data distributed in different independent medical institutions to ensure that these data are stored independently. As a distributed accounting technology for multi-party maintenance and backup information security, blockchain is a good breakthrough point for innovation in medical data sharing. In this paper, the system realizes the personal health data centre on the Hadoop big data platform, and the original distributed data are stored and analyzed centrally through the data synchronization module and the independent data acquisition system. Utilizing the advantages of the Hadoop big data platform, the personalized health information system for stroke has designed to provide personalized health management services for patients and facilitate the management of patients by medical staff.

Lamine Amour, Matthieu Quiniou, Sara Tucci-Piergiovanni, Hichem Bourak · 5 authors

No abstract is available for this record.

Naeem Iqbal, Faisal Jamil, Shabir Ahmad, Do‐Hyeun Kim

The recent advances in information management systems coupled with machine learning algorithms paved the way for a significant revolution in animal healthcare industries. However, the data in such systems suffer from various challenges such as security, reliability, and convenience, to name a few. Traditional systems are not useful to meet these critical issues because these systems have not a consistent structure for data security and reliability policies. Therefore, a new solution is required to enhance data accessibility and should regulate government security policies to ensure the accountability of the usage of the medical records system. Moreover, it is also required to analyze historical data of veterinary clinic using data mining and machine learning techniques to predict the future appointments scheduling requests, which is essential for veterinary management to drive better future decisions, for instance, future demands of medical supplies and to plan veterinary medical staff, etc. This paper aims to fill the gap by proposing a novel blockchain-based reliable and intelligent veterinary information management system (RIVIMS) using smart contract and machine learning techniques. The proposed RIVIMS consists of two main modules; blockchain-based secured veterinary information management, data and predictive analytics modules. First, a blockchain-based secure and reliable veterinary clinic information management system is developed using Hyperledger Fabric. Second, a smart contract enabled data, and predictive analytics modules are developed using permissioned blockchain framework. The data and predictive modules aim to analyze veterinary clinic patients appointments data in order to discover underlying patterns and build a robust prediction model using machine learning algorithms. The data and predictive helps veterinary management to drive better future business decisions to provide better healthcare services to veterinary patients. Hyperledger Caliper is used as a benchmark tool to evaluate the performance of the developed blockchain-based system in terms of transaction per second, transaction success rate, transaction throughput, and transaction latency. Furthermore, machine learning performance measures have utilized, such as MAE, RMSE, and R2 score to evaluate the overall performance of the prediction model. The experimental results demonstrate the effectiveness and robustness of the proposed RIVIMS.

P. G. Shynu, Varun G. Menon, R. Lakshmana Kumar, Seifedine Kadry · 5 authors

Fog computing is a modern computing model which offers geographically dispersed end-users with the latency-aware and highly scalable services. It is comparatively safer than cloud computing, due to information being rapidly stored and evaluated closer to data sources on local fog nodes. The advent of Blockchain (BC) technology has become a remarkable, most revolutionary, and growing development in recent years. BT’s open platform stresses data protection and anonymity. It also guarantees data is protected and valid through the consensus process. BC is mainly used in money-related exchanges; now it will be used in many domains, including healthcare; This paper proposes efficient Blockchain-based secure healthcare services for disease prediction in fog computing. Diabetes and cardio diseases are considered for prediction. Initially, the patient health information is collected from Fog Nodes and stored on a Blockchain. The novel rule-based clustering algorithm is initially applied to cluster the patient health records. Finally, diabetic and cardio diseases are predicted using feature selection based adaptive neuro-fuzzy inference system (FS-ANFIS). To evaluate the performance of the proposed work, an extensive experiment and analysis were conducted on data from the real world healthcare. Purity and NMI metrics are used to analyze the performance of the rule based clustering and the accuracy is used for prediction performance. The experimental results show that the proposed work efficiently predicts the disease. The proposed work reaches more than 81% of prediction accuracy compared to the other neural network algorithms.