Diet Recommendation System for Kidney Disease Patients Using Collaborative Filtering
DOI:
https://doi.org/10.35335/jict.v16i2.265Keywords:
Chronic Kidney Disease, Collaborative Filtering, Content-Based Approach, Dietary Recommendation System, Personalized NutritionAbstract
Chronic kidney disease (CKD) remains a major global health challenge, particularly in Indonesia, where limited awareness and inadequate dietary management contribute to the progression of renal complications. Patients often face difficulties in selecting foods that meet both medical and nutritional requirements, underscoring the need for intelligent dietary guidance. This study aims to develop a personalized dietary recommendation system for kidney failure patients using a hybrid approach that combines content-based and collaborative filtering techniques. The model was designed to analyze patients’ food preferences, nutritional composition, and health conditions to generate appropriate dietary recommendations. The system’s performance was evaluated using cosine similarity and predictive accuracy metrics, including RMSE, precision, recall, and F1-score. The results show that the proposed model achieved an accuracy of 83%, precision of 75%, recall of 100%, and F1-score of 86%, demonstrating its effectiveness in identifying dietary similarities and preferences among patients with comparable clinical profiles. Furthermore, by integrating nutritional content data such as sodium, potassium, and protein levels, the system successfully provided clinically safe and personalized recommendations aligned with renal dietary guidelines. These findings highlight the potential of artificial intelligence–based recommendation systems to support dietitians in improving the accuracy and efficiency of nutritional counseling, thereby promoting patient adherence and enhancing the quality of kidney disease management in hospital settings.
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