OPTIMIZING WELLNESS ITINERARIES IN UZBEKISTAN'S SANATORIUMS USING MACHINE LEARNING-BASED HEALTH PROFILING - Sorokin , S; Aganova , V (Uzbekistan Open Conference, 2025)
Health tourism in Uzbekistan is gaining prominence due to its historic sanatoriums offering wellness treatments like mineral baths and therapeutic massages. This paper proposes a machine learning-based recommendation system to design personalized wellness itineraries for tourists visiting Uzbekistan’s sanatoriums in Tashkent and Samarkand. By analyzing health data (vital signs, medical history, and preferences), we employ collaborative filtering and clustering techniques to match tourists with treatments aligned to their health goals, such as stress reduction or cardiovascular health. Using synthetic datasets, we develop and evaluate a model implemented in Python with scikit-learn, addressing challenges like data scarcity and cultural alignment in Uzbekistan’s tourism sector. Results demonstrate high model accuracy (85% recommendation relevance) and positive user satisfaction (4.2/5 rating). The system enhances personalization in Uzbekistan’s wellness tourism, contributing to adaptive and equitable health ecosystems.
Keywords: health tourism; machine learning; personalized recommendation; Uzbekistan sanatoriums; wellness itineraries; collaborative filtering; clustering