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Fusion of Health Care Architecture for Predicting Vulnerable Diseases Using Automated Decision Support Systems

Fusion of Health Care Architecture for Predicting Vulnerable Diseases Using Automated Decision Support Systems

Abirami L., Karthikeyan J.
Copyright: © 2019 |Volume: 11 |Issue: 2 |Pages: 14
ISSN: 1938-0194|EISSN: 1938-0208|EISBN13: 9781522565192|DOI: 10.4018/IJWP.2019070104
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MLA

Abirami L., and Karthikeyan J. "Fusion of Health Care Architecture for Predicting Vulnerable Diseases Using Automated Decision Support Systems." IJWP vol.11, no.2 2019: pp.53-66. http://doi.org/10.4018/IJWP.2019070104

APA

Abirami L. & Karthikeyan J. (2019). Fusion of Health Care Architecture for Predicting Vulnerable Diseases Using Automated Decision Support Systems. International Journal of Web Portals (IJWP), 11(2), 53-66. http://doi.org/10.4018/IJWP.2019070104

Chicago

Abirami L., and Karthikeyan J. "Fusion of Health Care Architecture for Predicting Vulnerable Diseases Using Automated Decision Support Systems," International Journal of Web Portals (IJWP) 11, no.2: 53-66. http://doi.org/10.4018/IJWP.2019070104

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Abstract

The healthcare industry is a stage which is presented with tremendous innovative headways consistently. With the perfect learning of foundation data, writing, and proposed calculation, the proposition conveys engineering for supporting computerized choices to medicinal services organizations. Electronic records are constantly gathered and sorted out to give a point by point history of patients, their sicknesses and determination plans. From the acquired data, the virtual doctoring engine (VDE) endeavors to break down the discernible attributes from the datasets utilizing the known-yet-predict (KYP) calculation to propose an ideal finding plan. This treatment plan will later be directed by a specialist for treating the patients. The exhibition of VDE framework is tried against patients experiencing cardiovascular infections. This methodology has been examined against different component extraction calculations and observed to be 18.2% progressively exact in anticipating the ideal treatment plan.

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