A comprehensive analysis of healthcare big data management, analytics and scientific programming

Healthcare systems are transformed digitally with the help of medical technology, information systems, electronic medical records, wearable and smart devices, and handheld devices. The advancement in the medical big data, along with the availability of new computational models in the field of healthcare, has enabled the caretakers and researchers to extract relevant information and visualize the healthcare big data in a new spectrum. The role of medical big data becomes a challenging task in the form of storage, required information retrieval within a limited time, cost efficient solutions in terms care, and many others. Early decision making based healthcare system has massive potential for dropping the cost of care, refining quality of care, and reducing waste and error. Scientific programming play a significant role to overcome the existing issues and future problems involved in the management of large scale data in healthcare, such as by assisting in the processing of huge data volumes, complex system modelling, and sourcing derivations from healthcare data and simulations. Therefore, to address this problem efficiently a detailed study and analysis of the available literature work is required to facilitate the doctors and practitioners for making the decisions in identifying the disease and suggest treatment accordingly. The peer reviewed reputed journals are selected for the accumulated of published research work during the period ranges from 2015 - 2019 (a portion of 2020 is also included). A total of 127 relevant articles (conference papers, journal papers, book section, and survey papers) are selected for the assessment and analysis purposes. The proposed research work organizes and summarizes the existing published research work based on the research questions defined and keywords identified for the search process. This analysis on the existence research work will help the doctors and practitioners to make more authentic decisions, which ultimately will help to use the study as evidence for treating patients and suggest medicines accordingly

Towards a Domain – Specific Comparative Analysis of Data Mining Tools

Advancement in technology has brought in widespread adoption and utilization of data mining tools. Successful implementation of data mining requires a careful assessment of the various data mining tools. Although several works have compared data mining tools based on usability, opensource, integrated data mining tools for statistical analysis, big/small scale, and data visualization, none of them has suggested the tools for various industry-sectors. This paper attempts to provide a comparative study of various data mining tools based on popularity and usage among various industry-sectors such as business, education, and healthcare. The factors used in the comparison are performance and scalability, data access, data preparation, data exploration and visualization, advanced modeling capabilities, programming language, operating system, interfaces, ease of use, and price/license. The following popular data mining tools are assessed: SAS Enterprise Miner, KNIME, and R for business, Moodle Learning Analytics, Blackboard Analytics, and Canvas for education, and RapidMiner, IBM Watson Health, and Tableau for healthcare. It also discusses the critical issues and challenges associated with the adoption of data mining tools. Furthermore, it suggests possible solutions to help various industries choose the best data mining tool that covers their respective data mining requirements