Prediction of Disease Using Machine Learning over Big Data-Survey

With massive information development in medical specialty and aid community, precise analysis of medical information advantages premature disease detection, patient care and community services. although, the analysis accuracy is reduced once the standard of medical information is incomplete. moreover, completely different regions exhibit distinctive characteristics of bound regional diseases, which can weaken the prediction of illness outbreaks. during this paper, we tend to contour machine learning algorithms for effective prediction of chronic malady eruption in disease-frequent communities. we tend to experiment the tailored prediction models over real-life hospital information collected from central China in 2013-2015. to beat the problem of incomplete information, we tend to use a latent issue model to build the missing information. we tend to experiment on a regional chronic illness of cerebral infarction. we tend to propose a replacement convolutional neural network based multimodal disease risk prediction (CNN-MDRP) algorithmic program victimisation structured and unstructured information from hospital. To the simplest of our data, none of the prevailing work targeted on each information varieties within the space of medical massive information analytics. Compared to many typical prediction algorithms, the prediction accuracy of our projected algorithmic program reaches ninety four.8% with a convergence speed that is faster than that of the CNN-based unimodal disease risk prediction (CNN-UDRP) algorithmic program

Mist Data: Leveraging Mist Computing for Secure and Scalable Architecture for Smart and Connected Health

The smart health paradigms employ Internet-connected wearables for tele-monitoring, diagnosis providing inexpensive healthcare solutions. Mist computing reduces latency and increases throughput by processing data near the edge of the network. In the present paper, we proposed a secure mist Computing architecture that is validated on recently released public geospatial health dataset. Results and discussion support the efficacy of proposed architecture for smart geospatial health applications. The present research paper proposed SoA-Mist i.e. a three-tier secure framework for efficient management of geospatial health data with the use of mist devices. It proposed the security aspects in client layer, mist layer, fog layer and cloud layer. It has defined the prototype development by using win-win spiral model with use case and sequence diagram. Overlay analysis has been performed with the developed framework on malaria vector borne disease positive maps of Maharastra state in India from 2011 to 2014 in mobile clients as test case. Finally, It concludes with the comparison analysis of cloud based framework and proposed SoA-Mist framework

SciTech News Volume 71, No. 1 (2017)

Columns and Reports From the Editor 3 Division News Science-Technology Division 5 Chemistry Division 8 Engineering Division Aerospace Section of the Engineering Division 9 Architecture, Building Engineering, Construction and Design Section of the Engineering Division 11 Reviews Sci-Tech Book News Reviews 12 Advertisements IEEE

Health on a Cloud: Modeling Digital Flows in an E-health Ecosystem

A unified and well-knit e-health network is one that provides a common platform to its key stakeholders to facilitate a sharing of information with a view to promoting cooperation and maximizing benefits. A promising candidate worthy of being considered for this ponderous job is the emerging "cloud technology" with its offer of computing as a utility, which seems well-suited to foster such a network bringing together diverse players who would otherwise remain fragmented and be unable to reap benefits that accrue from cooperation. The e-health network serves to provide added value to its various stakeholders through syndication, aggregation and distribution of this health information, thereby reducing costs and improving efficiencies. Because such a network is in fact an interconnected "network of network" that delivers a product or service through both competition and cooperation, it can be thought of as a business ecosystem. . This study attempts to model the digital information flows in an e-health ecosystem and analyze the resulting strategic implications for the key players for whom the rules of the game are bound to change given their interdependent added-values. The ADVISOR framework is deployed to examine the values created and captured in the ecosystem. Based on this analysis, some critical questions that must be addressed as necessary preconditions for an e-Health Cloud, are derived. The paper concludes with the conjecture that "collaboration for value" will replace "competition for revenue" as the new axiom in the health care business that could ideally usher in a fair, efficient and sustainable ecosystem