title: Prediction of cold and heat patterns using anthropometric measures based on machine learning.
abstract: OBJECTIVE
  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.
  
  
  METHODS
  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.
  
  
  RESULTS
  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.
  
  
  CONCLUSIONS
  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.
authors.affiliation: Medical Research Division, Korea Institute of Oriental Medicine, Deajeon, 305-811, Republic of Korea.
authors.forename: Bum Ju
authors.initials: BJ
authors.lastname: Lee
authors.affiliation: Medical Research Division, Korea Institute of Oriental Medicine, Deajeon, 305-811, Republic of Korea.
authors.forename: Jae Chul
authors.initials: JC
authors.lastname: Lee
authors.affiliation: Medical Research Division, Korea Institute of Oriental Medicine, Deajeon, 305-811, Republic of Korea.
authors.forename: Jiho
authors.initials: J
authors.lastname: Nam
authors.affiliation: Medical Research Division, Korea Institute of Oriental Medicine, Deajeon, 305-811, Republic of Korea. ssmed@kiom.re.kr.
authors.forename: Jong Yeol
authors.initials: JY
authors.lastname: Kim
mesh_terms: D000886:Anthropometry; D003080:Cold Temperature; D005260:Female; D006358:Hot Temperature; D006801:Humans; D016015:Logistic Models; D000069550:Machine Learning; D008297:Male
keywords: cold and heat patterns; cold syndrome; data mining; machine learning; personalized medicine; predictive power