Modelling Baby's Health Management System Using Rule-Based Algorithm

Abstract

In Malaysia, the growth of a new-born baby is recorded in a health handbook. The data in the handbook is consistently monitored through the information attained by nurses and doctors. Details such as the baby's weight, height, immunization schedule and growth development are all recorded into the handbook. On that account, the handbook needs to be brought when having an appointment as it is recorded manually and handwritten by nurses and doctors alike. However, in some cases the probability of losing the handbook is high, hence important information such as the child's appointment dates and health data can be overlooked and missed by parents and caregivers. Moreover, with the loss of key data, parents may face difficulties identifying symptoms that might present on their baby. Therefore, this study has developed a system to record all of the necessary data. It was modelled using a rule-based algorithm to assess the condition of a baby based on their symptoms, the system includes a postpartum confinement module to support mother's regarding their postpartum confinement practices. The system also serves as a reminder for parents regarding their baby's next vaccination appointment. Results shows that the system is able to improve the disadvantages of the old manual approach, allowing for maximum data storage while reducing data entering discrepancies. By using a rule-based approach, the system can model a baby's health state and keep track of immunisation schedules as well as deliver friendly reminders to parents and caregivers if the need arises.