Comparative Analysis of the Certainty Factor and Dempster-Shafer Methods in the Diagnosis of Acute Respiratory Infection in Childern
Abstract
Acute Respiratory Infections (ARI) are one of the diseases that often affect children and are a major cause of morbidity and mortality in Indonesia. Accurate early diagnosis is very important to prevent complications, but limited medical personnel and the similarity of ARI symptoms to other diseases are often obstacles. In this context, an artificial intelligence-based expert system can be a solution to support medical decisions. This article presents a comparative analysis of two inference methods commonly used in expert systems, namely Certainty Factor (CF) and Dempster-Shafer (DS). Through a Systematic Literature Review (SLR) approach, this study evaluates the performance of both methods based on accuracy, complexity, flexibility, and ease of implementation. The results of the study show that Certainty Factor excels in simplicity and efficiency, while Dempster-Shafer is more reliable in handling uncertainty and cases with many overlapping symptoms. This article is expected to be a reference for the development of more accurate and efficient medical expert systems in assisting the diagnosis of ARI in children.
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