A Classification model leveraging Electronic Immunization Records to predict child immunization completion: case study - Mukono Health facility

dc.contributor.authorKembabazi, Bertha
dc.descriptionA Thesis Submitted to the Faculty of Information in partial fulfillment of the requirements for the award of Master of Science in Information Technologyen_US
dc.description.abstractImmunisation is one of the most cost-effective public health interventions, it prevents child deaths by strengthening immune response and preventing diseases that are not only deadly but also easily transmitted. However, countries like Uganda still face challenges that limit the attainment of immunisation completion targets like late and missed doses. It is noted that many children who start immunization do not follow through to the last dose which leads to incomplete doses hence no full protection and also missing some vaccinations which protect the child against other diseases, this in the long run exposes the child to the risk of contracting deadly diseases as well as spreading the same to others. There is potential to use data from electronic immunisation records systems to get projection insight to follow up on participants to increase access to immunisation. This study uses a random forest classification algorithm to develop a model to predict completion rates of infant immunisation to improve immunisation service delivery and utilization. This model predicts those likely to complete the recommended immunisation vaccines as per the schedule using DPT3 as an identifier classified into three categories. The categories were coded as 3 for those likely to miss, 1 for those who will receive on-time and 2 for those who are likely to receive the scheduled vaccine late. Using existing secondary electronic immunisation records data from the MyChild System implemented at Mukono district health facility, the data used was collected between 2015 and 2020. 75% of the data was used as training data while the other 25% was used as test data for the model. The predictors of this model include child dates of vaccine dose administration, the exposure to tetanus, whether a child was exposed to HIV, the date of birth and whether the caregiver was counselled. The model was tested and validated to give accurate predictions and the measure of accuracy as an output.en_US
dc.publisherStrathmore Universityen_US
dc.subjectRandom forestsen_US
dc.titleA Classification model leveraging Electronic Immunization Records to predict child immunization completion: case study - Mukono Health facilityen_US