Prediction of Shigellosis outcomes in Israel using machine learning classifiers

G. Adamker, T. Holzer, I. Karakis, M. Amitay, E. Anis, S. R. Singer, Z. Barnett-Itzhaki

Research output: Contribution to journalArticlepeer-review

7 Scopus citations

Abstract

Shigellosis causes significant morbidity and mortality in developing and developed countries, mostly among infants and young children. The World Health Organization estimates that more than one million people die from Shigellosis every year. In order to evaluate trends in Shigellosis in Israel in the years 2002-2015, we analysed national notifiable disease reporting data. Shigella sonnei was the most commonly identified Shigella species in Israel. Hospitalisation rates due to Shigella flexenri were higher in comparison with other Shigella species. Shigella morbidity was higher among infants and young children (age 0-5 years old). Incidence of Shigella species differed among various ethnic groups, with significantly high rates of S. flexenri among Muslims, in comparison with Jews, Druze and Christians. In order to improve the current Shigellosis clinical diagnosis, we developed machine learning algorithms to predict the Shigella species and whether a patient will be hospitalised or not, based on available demographic and clinical data. The algorithms' performances yielded an accuracy of 93.2% (Shigella species) and 94.9% (hospitalisation) and may consequently improve the diagnosis and treatment of the disease.

Original languageEnglish
Pages (from-to)1445-1451
Number of pages7
JournalEpidemiology and Infection
Volume146
Issue number11
DOIs
StatePublished - 1 Aug 2018
Externally publishedYes

Keywords

  • Epidemiology
  • Shigella
  • health statistics

ASJC Scopus subject areas

  • Epidemiology
  • Infectious Diseases

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