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Prediction of cold and heat patterns using anthropometric measures based on machine learning
- Chinese Journal of Integrative Medicine Vol. 24, Issue 1, Pages: 16-23(2018)
- Affiliations：
Medical Research Division, Korea Institute of Oriental Medicine, Deajeon, Republic of Korea
- Author bio：
- Funds：Supported by the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT & Future Planning (No. 2006-2005173, NRF-2012-0009830, and NRF-2009-0090900) and by the Bio & Medical Technology Development Program of the NRF funded by the Korean government, MSIP (No. NRF-2015M3A9B6027139)
DOI：10.1007/s11655-016-2641-8
CLC：Published：2018，
Published Online：29 December 2016，
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Lee, B.J., Lee, J.C., Nam, J. et al. Prediction of cold and heat patterns using anthropometric measures based on machine learning., Chin. J. Integr. Med. 24, 16–23 (2018). https://doi.org/10.1007/s11655-016-2641-8
Bum Ju Lee, Jae Chul Lee, Jiho Nam, et al. Prediction of cold and heat patterns using anthropometric measures based on machine learning. [J]. Chinese Journal of Integrative Medicine 24(1):16-23(2018)
Lee, B.J., Lee, J.C., Nam, J. et al. Prediction of cold and heat patterns using anthropometric measures based on machine learning., Chin. J. Integr. Med. 24, 16–23 (2018). https://doi.org/10.1007/s11655-016-2641-8 DOI：
Bum Ju Lee, Jae Chul Lee, Jiho Nam, et al. Prediction of cold and heat patterns using anthropometric measures based on machine learning. [J]. Chinese Journal of Integrative Medicine 24(1):16-23(2018) DOI： 10.1007/s11655-016-2641-8.
摘要
To examine the association of body shape with cold and heat patterns
to determine which anthropometric measure is the best indicator for discriminating between the two patterns
and to investigate whether using a combination of measures can improve the predictive power to diagnose these patterns. Based on a total of 4
859 subjects (3
000 women and 1
859 men)
statistical analyses using binary logistic regression were performed to assess the significance of the difference and the predictive power of each anthropometric measure
and binary logistic regression and Naive Bayes with the variable selection technique were used to assess the improvement in the predictive power of the patterns using the combined measures. In women
the strongest indicators for determining the cold and heat patterns among anthropometric measures were body mass index (BMI) and rib circumference; in men
the best indicator was BMI. In experiments using a combination of measures
the values of the area under the receiver operating characteristic curve in women were 0.776 by Naive Bayes and 0.772 by logistic regression
and the values in men were 0.788 by Naive Bayes and 0.779 by logistic regression. Individuals with a higher BMI have a tendency toward a heat pattern in both women and men. The use of a combination of anthropometric measures can slightly improve the diagnostic accuracy. Our findings can provide fundamental information for the diagnosis of cold and heat patterns based on body shape for personalized medicine.
Abstract
To examine the association of body shape with cold and heat patterns
to determine which anthropometric measure is the best indicator for discriminating between the two patterns
and to investigate whether using a combination of measures can improve the predictive power to diagnose these patterns. Based on a total of 4
859 subjects (3
000 women and 1
859 men)
statistical analyses using binary logistic regression were performed to assess the significance of the difference and the predictive power of each anthropometric measure
and binary logistic regression and Naive Bayes with the variable selection technique were used to assess the improvement in the predictive power of the patterns using the combined measures. In women
the strongest indicators for determining the cold and heat patterns among anthropometric measures were body mass index (BMI) and rib circumference; in men
the best indicator was BMI. In experiments using a combination of measures
the values of the area under the receiver operating characteristic curve in women were 0.776 by Naive Bayes and 0.772 by logistic regression
and the values in men were 0.788 by Naive Bayes and 0.779 by logistic regression. Individuals with a higher BMI have a tendency toward a heat pattern in both women and men. The use of a combination of anthropometric measures can slightly improve the diagnostic accuracy. Our findings can provide fundamental information for the diagnosis of cold and heat patterns based on body shape for personalized medicine.
关键词
cold and heat patternspersonalized medicinecold syndromepredictive powermachine learningdata mining
Keywords
cold and heat patternspersonalized medicinecold syndromepredictive powermachine learningdata mining
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