Integration of local wisdom and modern medicine in a treatment recommendation system for toddlers based on the Case-Based Reasoning-Fuzzy Method

Abstract

This study aims to develop a treatment recommendation system for toddlers based on Case-Based Reasoning (CBR) combined with Fuzzy Logic, by integrating modern medical knowledge and local wisdom. The system was developed to address the need for adaptive initial diagnosis recommendations, particularly in addressing ambiguous symptoms. At the case representation stage, disease, symptom, and drug data from medical and traditional perspectives are used as the knowledge base. The CBR process serves as the primary mechanism for searching for similar cases, while fuzzy logic is used at the revision stage to provide degrees of symptom intensity so that the diagnosis results are more flexible. System evaluation was conducted through blackbox testing, accuracy measurements, and the System Usability Scale (SUS) method involving 50 respondents. The results showed that all system functions ran as planned, the accuracy level reached 88%, and the average SUS score was 78.4 in the Good Usability category, indicating the system is easy to use and user-acceptable. This study proves that the CBR–Fuzzy integration is effective in providing accurate, adaptive, and culturally relevant initial diagnosis recommendations. For further research, it is recommended to expand the case base, refine fuzzy rules, develop a broader interface, and implement the system on a mobile platform to improve accuracy, ease of access, and wider user acceptance.