Major depressive disorder is often treated with medication. These antidepressants can cause side effects affecting health and lifestyle. Dietary interventions can support traditional treatments, enhancing overall health. However, current food recommendation systems lack personalized menu options for those with mental health issues. This research focuses on designing a dietary recommendation model for depression. The main theoretical implication is a novel food menu inferring process using a food knowledge graph with semantic rules called FONDUE (FOod kNwleDge graphs with semantics rUlEs). The design of the food knowledge graph focuses on dietary restrictions in both mental and physical conditions. The semantic rules are created using descriptive logic and the SPARQL query language to infer diets appropriate for patients with depression. The FONDUE was evaluated by analyzing thirty common patient case studies, each representing different physical conditions in patients with depression. The evaluation results revealed that the accuracy of the retrieved relevant menus was high, with an average precision of 0.86. However, the completeness of the retrieved relevant menus was moderate, with an average recall of 0.67. The lower recall observed can be attributed to the numerous menus within the model, which results in many relevant options still needing to be extracted. The average F -measure, which reflects a balance between precision and recall, scored 0.74. Compared with ChatGPT and Google Gemini, FONDUE demonstrated superior performance. Consequently, the personalized dietary recommendation model effectively suggests food menus tailored to address patients’ physical and mental health needs, instilling confidence in its efficacy.
Saengsupawat et al. (2026) studied this question.