Increase in the number of casualties visit to Emergency Department (ED) have lead to over crowd and delay in medical care. Hence, electronic triaging has been deployed to alleviate these problems and improve managing the patient. In this paper research methodology framework based on diagnostic and cross-sectional study is used for patient triage. The empirical approach is used to build models for patient triage to correctly predict the patient’s medical condition, given their signs and symptoms. Models are built with supervised learning algorithms. The “Naive Bayes”, “Support Vector Machine”, “Decision Tree” and, “Neural Network” classification models are implemented and evaluated using chi-square statistical test. This study infers the significance of using machine learning algorithms to predict patient’s medical condition. Support Vector Machine and Decision Tree have shown better performance for the considered dataset.

Original languageEnglish
Title of host publicationApplications and Techniques in Information Security - 9th International Conference, ATIS 2018, Proceedings
EditorsQingfeng Chen, Jia Wu, Shichao Zhang, Changan Yuan, Lynn Batten, Gang Li
PublisherSpringer Verlag
Number of pages7
ISBN (Print)9789811329067
Publication statusPublished - 01-01-2018
Event9th International Conference on Applications and Techniques in Information Security, ATIS 2018 - Nanning, China
Duration: 09-11-201811-11-2018

Publication series

NameCommunications in Computer and Information Science
ISSN (Print)1865-0929
ISSN (Electronic)1865-0937


Conference9th International Conference on Applications and Techniques in Information Security, ATIS 2018

All Science Journal Classification (ASJC) codes

  • Computer Science(all)
  • Mathematics(all)


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