Mobile-Bayesian diagnostic system for childhood infectious diseases

Precious Iheme, Nicholas Omoregbe, Sanjay Misra*, Davies Adeloye, Adewole Adewumi

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

About 5.9 million children under the age of 5 died in 2015, Preterm birth, delivery complications and infections source a great number of neonatal deaths. the Sustainable Development goals (SDGs) 3.2 is to end preventable deaths of newborns and children under 5 years of age, with a target to reduce neonatal mortality to at least 12 per 1,000 live births and under-5 mortality to at least as low as 25 per 1,000 live births in all countries. However quality and accessible healthcare service is essential to achieve this goal whereas most undeveloped and developing countries still have poor access to quality healthcare. with the emergences on mobile computing and telemedicine, this work provide diagnostics alternative for childhood infectious diseases using Naïve Bayesian classier which has been proven to be efficient in handling uncertainty as regards learning of incomplete data. In this research, sample data was collected from hospitals to model a pediatric system using Naïve Bayes classifier, which produce a 70% accuracy level suitable for a decision support system. The model was also integrated into a SMS platform to enable ease of usage.

Original languageEnglish
Title of host publicationProceedings of the 8th International Conference on Applications of Digital Information and Web Technologies, ICADIWT 2017
EditorsRicardo Rodriguez Jorge, Diego Moises Almazo Perez, Pit Pichappan, Pit Pichappan, Pit Pichappan, Jolanta Mizera-Pietraszko
PublisherIOS Press
Pages109-118
Number of pages10
ISBN (Electronic)9781614997726
DOIs
Publication statusPublished - 1 Jan 2017
Event8th International Conference on Applications of Digital Information and Web Technologies, ICADIWT 2017 - Chihuahua, Mexico
Duration: 29 Mar 201731 Mar 2017

Publication series

NameFrontiers in Artificial Intelligence and Applications
Volume295
ISSN (Print)0922-6389

Conference

Conference8th International Conference on Applications of Digital Information and Web Technologies, ICADIWT 2017
CountryMexico
CityChihuahua
Period29/03/1731/03/17

Keywords

  • Childhood diseases
  • Diagnostic system
  • Machine learning
  • Mobile computing
  • Naïve Bayesian
  • SMS-based system

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