The healthcare data is an important asset and rich source of healthcare intellect. Medical databases, if created properly, will be large, complex, heterogeneous and time varying. The main challenge nowadays is to store and process this data efficiently so that it can benefit humans. Heterogeneity in the healthcare sector in the form of medical data is also considered to be one of the biggest challenges for researchers. Sometimes, this data is referred to as large-scale data or big data. Blockchain technology and the Cloud environment have proved their usability separately. Though these two technologies can be combined to enhance the exciting applications in healthcare industry. Blockchain is a highly secure and decentralized networking platform of multiple computers called nodes. It is changing the way medical information is being stored and shared. It makes the work easier, keeps an eye on the security and accuracy of the data and also reduces the cost of maintenance. A Blockchain-based platform is proposed that can be used for storing and managing electronic medical records in a Cloud environment.

Machine learning is a subset of Artificial Intelligence when combined with Data Mining techniques plays a promising role in the field of prediction. We live in an era where data generation is exponential with time but if the generated data is not put to work or not converted to knowledge data, its generation is of no use. Similarly, in Healthcare also, data availability is high, so is the need to extract the information from it for better prognosis, diagnosis, treatment, drug development, and overall healthcare. In this research, we have tried to focus more on diagnosis of Diabetes disease, which is one of the fastest growing chronic diseases all over the world as declared by World Health Organization in the year 2014. We have also tried to show the different techniques like Gradient Boosting, Logistic Regression and Naive Bayes, which can be used for the diagnosis of diabetes disease with attained accuracy as 86% for the Gradient Boosting, 79% for Logistic Regression and 77% for Naive Bayes.

Blockchain technology plays a significant role in the industrial development. Many industries can potentially benefit from the innovations blockchain decentralization technology and privacy protocols offer with regard to securing, data access, auditing and managing transactions within digital platforms. Blockchain is based on distributed and secure decentralized protocols in which there is no single authority, and no single point of control; the data blocks are generated, added, and validated by the nodes of the network themselves. This article provides insights into the current developments within blockchain technology and explores its ability to revolutionize the multiple industrial application areas such as supply chain industry, Internet of Things (IoT), healthcare, governance, finance and manufacturing. It investigates and provides insights into the security issues and threats related to the blockchain implementations by assessing the research through a systematic literature review. This article proposes possible solutions in detail for enhancing the security of the blockchain for industrial applications along with significant directions for future explorations. The study further suggests how in recent years the adoption of blockchain technology by multiple industrial sectors has gained momentum while in the finance sector it is touching new heights day by day.

Big data technology has gained attention in all fields, particularly with regard to research and financial institutions. This technology has changed the world tremendously. Researchers and data scientists are currently working on its applicability in different domains such as health care, medicine, and the stock market, among others. The data being generated at an unexpected pace from multiple sources like social media, health care contexts, and Internet of things have given rise to big data. Management and processing of big data represent a challenge for researchers and data scientists, as there is heterogeneity and ambiguity. Heterogeneity is considered to be an important characteristic of big data. The analysis of heterogeneous data is a very complex task as it involves the compilation, storage, and processing of varied data based on diverse patterns and rules. The proposed research has focused on the heterogeneity problem in big data. This research introduces the hybrid support vector machine (H-SVM) classifier, which uses the support vector machine as a base. In the proposed algorithm, the heterogeneous Euclidean overlap metric (HEOM) and Euclidean distance are introduced to form clusters and classify the data on the basis of ordinal and nominal values. The performance of the proposed learning classifier is compared with linear SVM, random forest, and k-nearest neighbor. The proposed algorithm attained the highest accuracy as compared to other classifiers.

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