Clinical Nursing Risk Assessment and Early Warning System based on Support Vector Machine

Authors

  • Prof. V. Sujatha
  • Prof.K. Prasanna
  • Prof. Edna Sweenie J
  • Prof.S. Maha Lakshmi
  • Prof.T. Poornima

Keywords:

Support vector machine; Clinical nursing; Riskassessment;Early warning(keywords).

Abstract

Clinical nursing entails several hazards. When the early warning system is really functioning, the threshold that the system sets for assessment is too imprecise, resulting in an excessively lengthy reaction time. Support vector machine-powered clinical nursing is aimed to address this problem. An early warning system and risk assessment. Combine the requirements of the early warning system, design the hardware connection circuit, use the C/S network architecture to obtain clinical care risk data, calculate the clinical care risk value, use support vector machines to set different levels of early warning thresholds, and finally design the risk evaluation signal formation hardware. The system's design has been completed. To conduct experiments, two risk assessment and early warning systems, as well as an experimental system, are employed. The planned early warning system has the fastest reaction time, according to the findings.

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Published

2019-06-30

How to Cite

Prof. V. Sujatha, Prof.K. Prasanna, Sweenie J, P. E. ., Prof.S. Maha Lakshmi, & Prof.T. Poornima. (2019). Clinical Nursing Risk Assessment and Early Warning System based on Support Vector Machine. The Journal of Contemporary Issues in Business and Government, 24(1), 92–99. Retrieved from https://cibgp.com/au/index.php/1323-6903/article/view/142

Issue

Vol. 24 No. 1 (2019): The journal of contemporary issues in business and government

Section

Articles

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