Visual Analytics of Multidimensional Oral Health Surveys: Data Mining Study

Area of Science:

• Oral Health Research
• Data Visualization
• Public Health Informatics

Background:

• Oral health surveys are crucial for disease prevention, treatment, and population health monitoring.
• Analyzing multidimensional oral health data presents significant challenges due to its complexity.
• Gaining comprehensive insights from diverse oral health survey perspectives is difficult.

Purpose of the Study:

• To develop an advanced visualization framework for visual analytics and deep data mining of multidimensional oral health surveys.
• To enable intuitive exploration and understanding of complex oral health datasets.
• To facilitate the identification of patterns and correlations within oral health data.

Main Methods:

• Embedding diseases and population groups into data portraits using multidimensional attributes.
• Conducting group classification and correlation pattern extraction to analyze relationships between diseases, behaviors, symptoms, and cognitions.
• Constructing a knowledge graph to integrate semantic information and graph query functions for feature exploration.

Main Results:

• Implementation of a visualization framework tailored for multidimensional oral health survey exploration.
• Integration of user-friendly interactions to create a visual analysis system.
• Empowerment of users to better understand and regulate oral health conditions.

Conclusions:

• A novel visualization framework with integrated user interactions is presented.
• The system allows for intuitive understanding and in-depth exploration of oral health data.
• Real-world case studies confirm the system's effectiveness in exploring oral diseases.