Prediction of Shigellosis outcomes in Israel using machine learning classifiers.

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  • Additional Information
    • Source:
      Publisher: Cambridge University Press Country of Publication: England NLM ID: 8703737 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1469-4409 (Electronic) Linking ISSN: 09502688 NLM ISO Abbreviation: Epidemiol Infect Subsets: MEDLINE
    • Publication Information:
      Original Publication: Cambridge Eng : Cambridge University Press
    • Subject Terms:
      Algorithms* ; Shigella boydii* ; Shigella dysenteriae* ; Shigella flexneri* ; Shigella sonnei*; Dysentery, Bacillary/*epidemiology; Adolescent ; Adult ; Aged ; Child ; Child, Preschool ; Christianity ; Dysentery, Bacillary/ethnology ; Dysentery, Bacillary/microbiology ; Dysentery, Bacillary/mortality ; Female ; Hospitalization ; Humans ; Incidence ; Infant ; Islam ; Israel/epidemiology ; Jews ; Logistic Models ; Machine Learning ; Male ; Middle Aged ; Models, Statistical ; Neural Networks, Computer ; Pattern Recognition, Automated ; Young Adult
    • 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.
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    • Contributed Indexing:
      Keywords: Epidemiology; Shigella; health statistics
    • Publication Date:
      Date Created: 20180609 Date Completed: 20190318 Latest Revision: 20230916
    • Publication Date:
      20240829
    • Accession Number:
      PMC9133678
    • Accession Number:
      10.1017/S0950268818001498
    • Accession Number:
      29880081