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Activity-based nutrition management model for healthcare using similar group analysis
Kyungyong Chung1, Jonghun Kim2
1Division of Computer Science and Engineering, Kyonggi University, Suwon-si 16227, Korea.
Summary
This study introduces an activity-based nutrition management model using cluster analysis for personalized healthcare. The developed system aims to combat obesity, reduce healthcare costs, and improve national health outcomes.
Area of Science:
- Health Informatics
- Data Science in Healthcare
- Nutrition Science
Background:
- Environmental factors like poor nutrition, inactivity, and stress necessitate effective healthcare nutrition management.
- Controlling nutrition is crucial for disease prevention and treatment.
Purpose of the Study:
- To propose an activity-based nutrition management model utilizing cluster analysis for personalized healthcare.
- To enhance healthcare management services through data-driven insights.
Main Methods:
- Cluster analysis of similar groups for nutrition management.
- Development of a real-time, activity-based nutrition management algorithm using big data.
- Integration of voice recognition for food input and Bluetooth Low Energy (BLE) for bio-signal collection.
- Improved daily recommended allowance algorithm using real-time activity data.
Main Results:
- A nutrition management service system with a human interface was designed and developed.
- The system's effectiveness was verified through its implementation.
Conclusions:
- The developed health model offers solutions for obesity.
- Potential to reduce medical costs and improve national public health.
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