Integration of Forward Chaining and Bayes’ Theorem in Expert System Development for Malnutrition Diagnosis

Abstract

 Malnutrition refers to an imbalance in nutritional intake and affects the development of the human body. Based on survey data from the Ministry of Health in 2022, the prevalence of malnutrition in Indonesia was 21.6%, exceeding the prevalence standard set by the WHO of 20%. This study aims to build an expert system that has the ability to diagnose malnutrition through the application of the forward chaining method and Bayes' theorem. The forward chaining method is used to develop rules based on the symptoms observed in the patient. Meanwhile, Bayes' theorem is used to calculate the probability of malnutrition based on the combination of symptoms that appear in the patient. The integration of these two methods is expected to increase the accuracy in diagnosing malnutrition. Based on the results of the study, it was found that the application of the forward chaining method and Bayes' theorem in the system was able to diagnose a sample data of 150 patients with an accuracy rate of 98.67%. This indicates that this expert system has the potential to assist healthcare professionals and the community in making diagnoses.