Visual Analytics of Multidimensional Oral Health Surveys: Data Mining Study

BACKGROUND: Oral health surveys largely facilitate the prevention and treatment of oral diseases as well as the awareness of population health status. As oral health is always surveyed from a variety of perspectives, it is a difficult and complicated task to gain insights from multidimensional oral health surveys. OBJECTIVE: We aimed to develop a visualization framework for the visual analytics and deep mining of multidimensional oral health surveys. METHODS: First, diseases and groups were embedded into data portraits based on their multidimensional attributes. Subsequently, group classification and correlation pattern extraction were conducted to explore the correlation features among diseases, behaviors, symptoms, and cognitions. On the basis of the feature mining of diseases, groups, behaviors, and their attributes, a knowledge graph was constructed to reveal semantic information, integrate the graph query function, and describe the features of intrigue to users. RESULTS: A visualization framework was implemented for the exploration of multidimensional oral health surveys. A series of user-friendly interactions were integrated to propose a visual analysis system that can help users further achieve the regulations of oral health conditions. CONCLUSIONS: A visualization framework is provided in this paper with a set of meaningful user interactions integrated, enabling users to intuitively understand the oral health situation and conduct in-depth data exploration and analysis. Case studies based on real-world data sets demonstrate the effectiveness of our system in the exploration of oral diseases.