Leveraging Big Data Analytics and Hadoop in Developing India&amp;apos;s Healthcare Services

Augustine, D. Peter

doi:10.5120/15719-4622

Cited by 34 publications

(16 citation statements)

References 9 publications

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“…This leaves big data to face issues related to the acquisition and cleansing of data into a standardized format to enable analysis and global sharing [13,17,23,25,27]. With globalization of data, big data will have to deal with a variety of standards, barriers of language, and different terminologies.…”

Section: Resultsmentioning

confidence: 99%

“…Big data can enable population health management from a local or global perspective [31,34]. This capability becomes more salient from the global perspective when considering the aging of the population and age-related health issues shared by many populations and subpopulations, many of which are underserved [17,19,21,24,28,32]. About 61% of the literature mentioned this opportunity.…”

Section: Resultsmentioning

confidence: 99%

“…Literature suggests that big data enables rapid capture of data and the conversion of primary, raw and unstructured data into meaningful information [15,17,31,34]. New knowledge can then be generated from high volumes of effective data, enabling reuse of the data [15,20,21,32,33].…”

Section: Resultsmentioning

confidence: 99%

“…This pass-through of savings will be seen across the spectrum of medicine [24,36] and the health care workforce [25]. Savings will be realized through more cost-effective treatments and monitoring to improve medication adherence [25,31] and through the reduction of costly transportation costs, as is experienced in cardiology [12,17,22,34]. About 36% of the literature mentioned this opportunity.…”

Section: Resultsmentioning

confidence: 99%

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Challenges and Opportunities of Big Data in Health Care: A Systematic Review

et al. 2016

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BackgroundBig data analytics offers promise in many business sectors, and health care is looking at big data to provide answers to many age-related issues, particularly dementia and chronic disease management.ObjectiveThe purpose of this review was to summarize the challenges faced by big data analytics and the opportunities that big data opens in health care.MethodsA total of 3 searches were performed for publications between January 1, 2010 and January 1, 2016 (PubMed/MEDLINE, CINAHL, and Google Scholar), and an assessment was made on content germane to big data in health care. From the results of the searches in research databases and Google Scholar (N=28), the authors summarized content and identified 9 and 14 themes under the categories Challenges and Opportunities, respectively. We rank-ordered and analyzed the themes based on the frequency of occurrence.ResultsThe top challenges were issues of data structure, security, data standardization, storage and transfers, and managerial skills such as data governance. The top opportunities revealed were quality improvement, population management and health, early detection of disease, data quality, structure, and accessibility, improved decision making, and cost reduction.ConclusionsBig data analytics has the potential for positive impact and global implications; however, it must overcome some legitimate obstacles.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

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Challenges and Opportunities of Big Data in Health Care: A Systematic Review

et al. 2016

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“…The games provide an environment shaped and driven in part by the outputs of the models, allowing users to exert influence over a limited set of parameters, and displaying the results when those actions cause changes in the underlying model. Augustine, D. Peter et al, [12], analyze and reveal the benefits of predictive analytics using Big Data and Hadoop in healthcare were huge amount of data is available in active as well as in passive form. In this work Big Data analytics and hadoop has been used to show that healthcare services to everyone can be provided at optimal cost.…”

Section: Litreature Reviewmentioning

confidence: 99%

Predictive Analytic Modeling: A Walkthrough

Shah¹,

Butt

Baba

2017

IJARCSSE

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Abstract-Predictive analytic modeling is the key step involved in building a successful Data Mining solution. Predictive analytic modeling is a set of iterative activities in which a predictive model is built from the observed data set. Predictive analytics is the area of data mining concerned with the prediction of future probabilities and trends using archive data. It brings together management, information technology and modeling that eases researchers in making suitable predictions. Many methodologies have been proposed for model building, and these are based on some basic industrial engineering frameworks. The proposed research work discusses CRISP, DMAIC and SEMMA methodologies which provide the building blocks for an efficient predictive model. Keywords-Predictive Analytics, Data Mining, Modeling, CRISP, DMAIC and SEMMA. I. INTRODUCTIONToday's demanding atmosphere is forcing more organizations to explore and adopt predictive analytics. Data being the indispensible assets of an organization is increasing exponentially and so does the data storage need. Selling large amount of data has little value, but if one can add insight via analytics to data then we have the opportunity to monetize it. There is a sea change in the corporate world, researchers and IT organizations can seize upon the opportunity to reinvent themselves and transform from a support function into a profit center [1]. "We are sitting on a mountain of gold but we're not mining it as effectively as we could," says Michael Masciandaro, director of business intelligence at Rohm & Haas, a global specialty materials manufacturer. Data Mining is the technique that retrieves useful information from large amounts of data. Data mining is defined as a process involving the extraction of useful and interesting information from the underlying data [2]. Modeling is the main part of Predictive Analytics. Predictive Analytics modeling is used in representation of real world situations for rendering or description of reality. Limited, imprecise, but useful, a model helps us to make sense of the world [3]. The result of the models may give important information that can be used in decision making and future prediction. Predictive Analytic Modeling helps in optimizing existing processes, better understanding and predicting customer behavior, identify unexpected opportunities and recessions, and predict problems before they happen. The core of predictive analytics relies on capturing relationships between explanatory variables and the predicted variables from past occurrences, and exploiting them to predict the unknown outcome. It is important to note, however, that the accuracy and usability of results will depend greatly on the level of data analysis and the quality of assumptions [4]. Predictive analytic modeling involves searching for meaningful relationships among variables and representing those relationships in models. These variables are called predictors and constitute the core element of predictive analytics. A predictor could be measured for an indi...

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