Diabetes is the leading cause of severe health complications and one of the top 10 causes of death worldwide. To date, diabetes has no cure, and therefore, it is necessary to take precautionary measures to avoid its occurrence. The main aim of this systematic review is to identify the majority of the risk factors for the incidence/prevalence of type 2 diabetes mellitus on one hand, and to give a critical analysis of the cohort/cross-sectional studies which examine the impact of the association of risk factors on diabetes. Consequently, we provide insights on risk factors whose interactions are major players in developing diabetes. We conclude with recommendations to allied health professionals, individuals and government institutions to support better diagnosis and prognosis of the disease.

Over the last decade, blockchain technology has emerged to provide solutions to the complexity and privacy challenges of using distributed databases. It reduces cost for customers by eliminating intermediaries and builds trust in peer-to-peer communications. Over this time, the concept of blockchain has shifted greatly due to its potential in business growth for enterprises and the rapidly evolving applications in a collaborative smart-city ecosystem, healthcare, and governance. Many platforms, with different architectures and consensus protocols, have been introduced. Consequently, it becomes challenging for an application developer to choose the right platform. Furthermore, blockchain has misaligned with the goals for an efficient green collaborative digital ecosystem. Therefore, it becomes critical to address this gap and to build new frameworks to align blockchain with those goals. In this paper, we discuss the evolution of blockchain architecture and consensus protocols, bringing a retrospective analysis and discussing the rationale of the evolution of the various architectures and protocols, as well as capturing the assumptions conducive to their development and contributions to building collaborative applications. We introduce a classification of those architectures helping developers to choose a suitable platform for applications and providing insights for future research directions in the field to build new frameworks.

Background: Over the last century, disruptive incidents in the fields of clinical and biomedical research have yielded a tremendous change in health data management systems. This is due to a number of breakthroughs in the medical field and the need for big data analytics and the Internet of Things (IoT) to be incorporated in a real-time smart health information management system. In addition, the requirements of patient care have evolved over time, allowing for more accurate prognoses and diagnoses. In this paper, we discuss the temporal evolution of health data management systems and capture the requirements that led to the development of a given system over a certain period of time. Consequently, we provide insights into those systems and give suggestions and research directions on how they can be improved for a better health care system. Objective: This study aimed to show that there is a need for a secure and efficient health data management system that will allow physicians and patients to update decentralized medical records and to analyze the medical data for supporting more precise diagnoses, prognoses, and public insights. Limitations of existing health data management systems were analyzed. Methods: To study the evolution and requirements of health data management systems over the years, a search was conducted to obtain research articles and information on medical lawsuits, health regulations, and acts. These materials were obtained from the Institute of Electrical and Electronics Engineers, the Association for Computing Machinery, Elsevier, MEDLINE, PubMed, Scopus, and Web of Science databases. Results: Health data management systems have undergone a disruptive transformation over the years from paper to computer, web, cloud, IoT, big data analytics, and finally to blockchain. The requirements of a health data management system revealed from the evolving definitions of medical records and their management are (1) medical record data, (2) real-time data access, (3) patient participation, (4) data sharing, (5) data security, (6) patient identity privacy, and (7) public insights. This paper reviewed health data management systems based on these 7 requirements across studies conducted over the years. To our knowledge, this is the first analysis of the temporal evolution of health data management systems giving insights into the system requirements for better health care. Conclusions: There is a need for a comprehensive real-time health data management system that allows physicians, patients, and external users to input their medical and lifestyle data into the system. The incorporation of big data analytics will aid in better prognosis or diagnosis of the diseases and the prediction of diseases. The prediction results will help in the development of an effective prevention plan.

Blockchain is a disruptive technology for shaping the next era of a healthcare system striving for efficient and effective patient care. This is thanks to its peer-to-peer, secure, and transparent characteristics. On the other hand, cloud computing made its way into the healthcare system thanks to its elasticity and cost-efficiency nature. However, cloud-based systems fail to provide a secured and private patient-centric cohesive view to multiple healthcare stakeholders. In this situation, blockchain provides solutions to address security and privacy concerns of the cloud because of its decentralization feature combined with data security and privacy, while cloud provides solutions to the blockchain scalability and efficiency challenges. Therefore a novel paradigm of blockchain-cloud integration (BcC) emerges for the domain of healthcare. In this paper, we provide an in-depth analysis of the BcC integration for the healthcare system to give the readers the motivations behind the emergence of this new paradigm, introduce a classification of existing architectures and their applications for better healthcare. We then review the development platforms and services and highlight the research challenges for the integrated BcC architecture, possible solutions, and future research directions. The results of this paper will be useful for the healthcare industry to design and develop a data management system for better patient care.

Diabetes, one of the top 10 causes of death worldwide, is associated with the interaction between lifestyle, psychosocial, medical conditions, demographic, and genetic risk factors. Predicting type 2 diabetes is important for providing prognosis or diagnosis support to allied health professionals, and aiding in the development of an efficient and effective prevention plan. Several works proposed machine-learning algorithms to predict type 2 diabetes. However, each work uses different datasets and evaluation metrics for algorithms’ evaluation, making it difficult to compare among them. In this paper, we provide a taxonomy of diabetes risk factors and evaluate 35 different machine learning algorithms (with and without features selection) for diabetes type 2 prediction using a unified setup, to achieve an objective comparison. We use 3 real-life diabetes datasets and 9 feature selection algorithms for the evaluation. We compare the accuracy, F-measure, and execution time for model building and validation of the algorithms under study on diabetic and non-diabetic individuals. The performance analysis of the models is elaborated in the article.