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Prediction of cold and heat patterns using anthropometric measures based on machine learning.
Bum Ju Lee1, Jae Chul Lee1, Jiho Nam1
1Medical Research Division, Korea Institute of Oriental Medicine, Deajeon, 305-811, Republic of Korea.
Higher body mass index (BMI) is linked to a heat pattern in both men and women. Combining anthropometric measures slightly improves diagnosis of cold and heat patterns, aiding personalized medicine.
Area of Science:
- Human physiology
- Anthropometry
- Personalized medicine
Background:
- Cold and heat patterns are traditional diagnostic categories.
- Body shape is often associated with these patterns.
- Identifying reliable anthropometric indicators is crucial for diagnosis.
Purpose of the Study:
- To investigate the association between body shape and cold/heat patterns.
- To identify the best anthropometric measure for differentiating these patterns.
- To assess if combined measures enhance diagnostic accuracy.
Main Methods:
- Statistical analysis of 4,859 subjects (3,000 women, 1,859 men).
- Binary logistic regression and Naive Bayes were employed.
- Variable selection techniques were used to evaluate combined measures.
Main Results:
- Body mass index (BMI) and rib circumference were key indicators in women; BMI was best in men.
- Area under the ROC curve values ranged from 0.772 to 0.788.
- Combined measures showed improved predictive power for pattern diagnosis.
Conclusions:
- Higher BMI correlates with a heat pattern in both sexes.
- Combining anthropometric measures offers a slight improvement in diagnostic accuracy.
- Findings support body shape-based diagnosis for personalized medicine.
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