Abstract
Acute Respiratory Tract Infections (ARTIs) in children are often challenging to diagnose due to their similar and varied symptoms. Hence, the development of an expert system for diagnosing ARTIs in children is crucial. The aim of this research is to develop an expert system that can assist in the rapid and accurate diagnosis of ARTIs in children. The method employed is the forward chaining approach, where observed symptoms are linked to specific ARTIs through inference rules. Initially, the symptoms of ARTIs and related diseases are identified, forming the knowledge base. Subsequently, the forward chaining algorithm is implemented in the system to facilitate the dissemination of information from observed symptoms to the correct diagnosis. The user interface is designed to facilitate input from users, whether doctors or parents, regarding their child's symptoms. Internal testing is conducted to validate the system's accuracy, involving medical professionals to evaluate and provide feedback on the generated diagnoses. The results indicate that bronchitis is the most likely disease in children based on the total scores of observed symptoms. Treatment recommendations focus on symptomatic care and complication prevention. Thus, the use of an expert system with a forward chaining approach positively contributes to supporting the diagnosis of ARTIs in children by providing faster and more accurate diagnoses and appropriate treatment recommendations.
Published Version
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call