Abstract:
Background: Diabetes mellitus affects insulin production or response and causes high blood
sugar levels. Personalized health/wellness recommendations are indispensable in world and are
provided by health recommender systems such as Singular Value Decomposition (SVD).
Objective: To create a food recommender system for type 2 diabetic patients using SVD
Methods: A questionnaire was designed with a validation process to obtain the information of the
participants in the interview, following a comprehensive literature review. A total of 917 patients,
at Matugama district hospital were selected using a random sampling method, and a complete
dataset containing fasting blood sugar (FBS) level, age, gender, weight, height, and preferences
for 50 diabetic-friendly food items (for Sri Lankan meal style) were obtained through a literature
review and consultation with a nutritional expert. Data were collected using direct patient
interviews and from data available in the literature. The SVD technique recommended food items
based on the Cosine Similarity Metric and user-based Collaborative Filtering. Most similar
patients to a particular patient were identified, and the highest-rated food items were ranked
considering the glycemic index and glycemic load. The system was implemented using the Python
programming language, with essential libraries such as NumPy and Pandas being leveraged and
Mean Absolute Error used as validation metric.
Results: Patients were assisted in achieving their blood glucose targets and the risk of unexpected
blood spikes and dips was reduced. Nutritional foods with low glycemic index and glycemic load
were recommended by the system based on FBS level and ratings given by patient for foods. A
specific cut-off FBS level of 126 mg/dL or higher is used to tailor recommendations. The ability to
handle a new patient is also incorporated and prediction accuracy of system was 89%,
demonstrating its strength and reliability in providing personalized dietary suggestions.
Conclusion: The SVD systems can be implemented to recommend food items for type 2 diabetic
patients, improving their adherence to dietary guidelines and managing their blood sugar levels
effectively. It is recommended for further modifications promoting the commercialization of
health informatics.
