Combination of forward chaining and certainty factor methods for the early detection of Acute Respiratory Infections (ARI)
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Abstract
Acute respiratory infections (ARI) encompass diseases with epidemic prevalence and mortality (measured number of deaths in a population) worldwide. Several types of diseases are classified as acute respiratory infections (ARI), i.e., pneumonia, diphtheria, tuberculosis, and COVID-19. The Ministry of Health of Indonesia suggests that one of the performance indicators for infectious disease control and management is disease detection. Disease detection constitutes the most significant factor for infectious diseases, and early detection based on the patient's symptoms is essential to classify and treat the diseases. The delayed treatment of these diseases has resulted in many cases and deaths, especially in ARI. Furthermore, to help support the Ministry of Health's program, an expert system was established to detect early acute respiratory infections (ARI) based on the patient's symptoms. Employing a combination of forward chaining and certainty factor as the expert system method could produce a percentage range of system accuracy of 95 to 97.5%.
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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
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