Enhanced Case-Based Reasoning Framework with Weighted Jaccard Similarity for Malnutrition Diagnosis in Toddlers
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Abstract
Malnutrition is a serious condition caused by nutrient deficiency that poses a high risk to toddler growth and development, potentially leading to long-term health problems or even death if left untreated. Early detection of malnutrition symptoms is crucial to enable prompt and appropriate medical interventions. This study aims to develop an expert system capable of diagnosing malnutrition diseases quickly, accurately, and efficiently, particularly as a knowledge-based decision support tool in toddler healthcare. The method used is Case Based Reasoning (CBR), which applies experiences from previous cases to solve new ones. The system processes data consisting of 22 symptoms and 8 types of malnutrition diseases, supported by a database of 22 real cases. Each symptom is associated with the likelihood of a disease based on its similarity to previous cases. Performance evaluation results show an accuracy of 80% and a sensitivity of 85.7%, indicating that the system is fairly reliable in recognizing positive cases (REUSE) and providing appropriate diagnoses. In conclusion, the CBR- based expert system can serve as an effective diagnostic aid for medical personnel in quickly identifying malnutrition in toddlers, thereby supporting more efficient and targeted decision-making.
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