Mobile-Bayesian Diagnostic System for Childhood Infectious Diseases

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 Naive 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 Naive 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.