Bum Ju Lee

Prediction of Fasting Plasma Glucose Status Using Anthropometric Measures for Diagnosing Type 2 Diabetes

It is well known that body fat distribution and obesity are important risk factors for type 2 diabetes. Prediction of type 2 diabetes using a combination of anthropometric measures remains a controversial issue. This study aims to predict the fasting plasma glucose (FPG) status that is used in the diagnosis of type 2 diabetes by a combination of various measures among Korean adults. A total of 4870 subjects (2955 females and 1915 males) participated in this study. Based on 37 anthropometric measures, we compared predictions of FPG status using individual versus combined measures using two machine-learning algorithms. The values of the area under the receiver operating characteristic curve in the predictions by logistic regression and naive Bayes classifier based on the combination of measures were 0.741 and 0.739 in females, respectively, and were 0.687 and 0.686 in males, respectively. Our results indicate that prediction of FPG status using a combination of anthropometric measures was superior to individual measures alone in both females and males. We show that using balanced data of normal and high FPG groups can improve the prediction and reduce the intrinsic bias of the model toward the majority class.

Prediction of body mass index status from voice signals based on machine learning for automated medical applications

OBJECTIVES The body mass index (BMI) provides essential medical information related to body weight for the treatment and prognosis prediction of diseases such as cardiovascular disease, diabetes, and stroke. We propose a method for the prediction of normal, overweight, and obese classes based only on the combination of voice features that are associated with BMI status, independently of weight and height measurements. MATERIALS AND METHODS A total of 1568 subjects were divided into 4 groups according to age and gender differences. We performed statistical analyses by analysis of variance (ANOVA) and Scheffe test to find significant features in each group. We predicted BMI status (normal, overweight, and obese) by a logistic regression algorithm and two ensemble classification algorithms (bagging and random forests) based on statistically significant features. RESULTS In the Female-2030 group (females aged 20-40 years), classification experiments using an imbalanced (original) data set gave area under the receiver operating characteristic curve (AUC) values of 0.569-0.731 by logistic regression, whereas experiments using a balanced data set gave AUC values of 0.893-0.994 by random forests. AUC values in Female-4050 (females aged 41-60 years), Male-2030 (males aged 20-40 years), and Male-4050 (males aged 41-60 years) groups by logistic regression in imbalanced data were 0.585-0.654, 0.581-0.614, and 0.557-0.653, respectively. AUC values in Female-4050, Male-2030, and Male-4050 groups in balanced data were 0.629-0.893 by bagging, 0.707-0.916 by random forests, and 0.695-0.854 by bagging, respectively. In each group, we found discriminatory features showing statistical differences among normal, overweight, and obese classes. The results showed that the classification models built by logistic regression in imbalanced data were better than those built by the other two algorithms, and significant features differed according to age and gender groups. CONCLUSION Our results could support the development of BMI diagnosis tools for real-time monitoring; such tools are considered helpful in improving automated BMI status diagnosis in remote healthcare or telemedicine and are expected to have applications in forensic and medical science.

A Classification Method of Normal and Overweight Females Based on Facial Features for Automated Medical Applications

Obesity and overweight have become serious public health problems worldwide. Obesity and abdominal obesity are associated with type 2 diabetes, cardiovascular diseases, and metabolic syndrome. In this paper, we first suggest a method of predicting normal and overweight females according to body mass index (BMI) based on facial features. A total of 688 subjects participated in this study. We obtained the area under the ROC curve (AUC) value of 0.861 and kappa value of 0.521 in Female: 21–40 (females aged 21–40 years) group, and AUC value of 0.76 and kappa value of 0.401 in Female: 41–60 (females aged 41–60 years) group. In two groups, we found many features showing statistical differences between normal and overweight subjects by using an independent two-sample t-test. We demonstrated that it is possible to predict BMI status using facial characteristics. Our results provide useful information for studies of obesity and facial characteristics, and may provide useful clues in the development of applications for alternative diagnosis of obesity in remote healthcare.

A New Method of Diagnosing Constitutional Types Based on Vocal and Facial Features for Personalized Medicine

The aim of the present study is to develop an accurate constitution diagnostic method based solely on the individuals physical characteristics, irrespective of psychologic traits, characteristics of clinical medicine, and genetic factors. In this paper, we suggest a novel method for diagnosing constitutional types using only speech and face characteristics. Based on 514 subjects, the area under the receiver operating characteristics curve (AUC) values of classification models in age and gender groups ranged from 0.64 to 0.89. We identified significant features showing statistical differences among three constitutional types by performing statistical analysis. Also, we selected a compact and discriminative feature subset for constitution diagnosis in each age and gender group. Our method may support the direction of improved diagnosis prediction and will serve to develop a personal and automatic constitution diagnosis software for improvement of the effectiveness of prescribed medications and development of personalized medicine.

Identification of Type 2 Diabetes Risk Factors Using Phenotypes Consisting of Anthropometry and Triglycerides based on Machine Learning

The hypertriglyceridemic waist (HW) phenotype is strongly associated with type 2 diabetes; however, to date, no study has assessed the predictive power of phenotypes based on individual anthropometric measurements and triglyceride (TG) levels. The aims of the present study were to assess the association between the HW phenotype and type 2 diabetes in Korean adults and to evaluate the predictive power of various phenotypes consisting of combinations of individual anthropometric measurements and TG levels. Between November 2006 and August 2013, 11 937 subjects participated in this retrospective cross-sectional study. We measured fasting plasma glucose and TG levels and performed anthropometric measurements. We employed binary logistic regression (LR) to examine statistically significant differences between normal subjects and those with type 2 diabetes using HW and individual anthropometric measurements. For more reliable prediction results, two machine learning algorithms, naive Bayes (NB) and LR, were used to evaluate the predictive power of various phenotypes. All prediction experiments were performed using a tenfold cross validation method. Among all of the variables, the presence of HW was most strongly associated with type 2 diabetes (p <; 0.001, adjusted odds ratio (OR) = 2.07 [95% CI, 1.72-2.49] in men; p <; 0.001, adjusted OR = 2.09 [1.79-2.45] in women). When comparing waist circumference (WC) and TG levels as components of the HW phenotype, the association between WC and type 2 diabetes was greater than the association between TG and type 2 diabetes. The phenotypes tended to have higher predictive power in women than in men. Among the phenotypes, the best predictors of type 2 diabetes were waist-to-hip ratio + TG in men (AUC by NB = 0.653, AUC by LR = 0.661) and rib-to-hip ratio + TG in women (AUC by NB = 0.73, AUC by LR = 0.735). Although the presence of HW demonstrated the strongest association with type 2 diabetes, the predictive power of the combined measurements of the actual WC and TG values may not be the best manner of predicting type 2 diabetes. Our findings may provide clinical information concerning the development of clinical decision support systems for the initial screening of type 2 diabetes.

A Novel Method for Classifying Body Mass Index on the Basis of Speech Signals for Future Clinical Applications: A Pilot Study

Obesity is a serious public health problem because of the risk factors for diseases and psychological problems. The focus of this study is to diagnose the patient BMI (body mass index) status without weight and height measurements for the use in future clinical applications. In this paper, we first propose a method for classifying the normal and the overweight using only speech signals. Also, we perform a statistical analysis of the features from speech signals. Based on 1830 subjects, the accuracy and AUC (area under the ROC curve) of age- and gender-specific classifications ranged from 60.4 to 73.8% and from 0.628 to 0.738, respectively. We identified several features that were significantly different between normal and overweight subjects (P < 0.05). Also, we found compact and discriminatory feature subsets for building models for diagnosing normal or overweight individuals through wrapper-based feature subset selection. Our results showed that predicting BMI status is possible using a combination of speech features, even though significant features are rare and weak in age- and gender-specific groups and that the classification accuracy with feature selection was higher than that without feature selection. Our method has the potential to be used in future clinical applications such as automatic BMI diagnosis in telemedicine or remote healthcare.

Prediction of cold and heat patterns using anthropometric measures based on machine learning

ObjectiveTo 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.MethodsBased 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.ResultsIn 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.ConclusionsIndividuals 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.

Identification of the Best Anthropometric Predictors of Serum High- and Low-Density Lipoproteins Using Machine Learning

Serum high-density lipoprotein (HDL) and low-density lipoprotein (LDL) cholesterol levels are associated with risk factors for various diseases and are related to anthropometric measures. However, controversy remains regarding the best anthropometric indicators of the HDL and LDL cholesterol levels. The objectives of this study were to identify the best predictors of HDL and LDL cholesterol using statistical analyses and two machine learning algorithms and to compare the predictive power of combined anthropometric measures in Korean adults. A total of 13 014 subjects participated in this study. The anthropometric measures were assessed with binary logistic regression (LR) to evaluate statistically significant differences between the subjects with normal and high LDL cholesterol levels and between the subjects with normal and low HDL cholesterol levels. LR and the naive Bayes algorithm (NB), which provides more reasonable and reliable results, were used in the analyses of the predictive power of individual and combined measures. The best predictor of HDL was the rib to hip ratio (p = <; 0.0001; odds ratio (OR) = 1.895; area under curve (AUC) = 0.681) in women and the waist to hip ratio (WHR) (p = <;0.0001; (OR) = 1.624; AUC = 0.633) in men. In women, the strongest indicator of LDL was age (p = <;0.0001; OR = 1.662; AUC by NB = 0.653); AUC byLR = 0.636. Among the anthropometric measures, the body mass index (BMI), WHR, forehead to waist ratio, forehead to rib ratio, and forehead to chest ratio were the strongest predictors of LDL; these measures had similar predictive powers. The strongest predictor in men was BMI (p = <;0.0001; OR = 1.369; AUC by NB = 0.594; AUC by LR = 0.595). The predictive power of almost all individual anthropometric measures was higher for HDL than for LDL, and the predictive power for both HDL and LDL in women was higher than for men. A combination of anthropometric measures slightly improved the predictive power for both HDL and LDL cholesterol. The best indicator for HDL and LDL might differ according to the type of cholesterol and the gender. In women, but not men, age was the variable that strongly predicted HDL and LDL cholesterol levels. Our findings provide new information for the development of better initial screening tools for HDL and LDL cholesterol.

Predictors of metabolic abnormalities in phenotypes that combined anthropometric indices and triglycerides.

BackgroundThe hypertriglyceridemic waist (HW) phenotype has been shown to be strongly associated with metabolic abnormalities; however, to date, no study has reported the prediction of metabolic abnormalities using the HW phenotype along with waist circumference (WC) and the triglyceride (TG) level or various phenotypes consisting of an individual anthropometric index combined with the TG level. The objectives of this study were to assess the association of the HW phenotype with metabolic abnormalities in Korean women and to evaluate the predictive powers of various phenotypes with regard to metabolic abnormalities.MethodsTotal cholesterol (TC), high- and low-density lipoprotein (HDL and LDL) cholesterol, and TG levels, systolic and diastolic blood pressures (SBP and DBP), and anthropometric indices were measured in 7661 women. The Naive Bayes algorithm and logistic regression were used to determine the predictive powers of the models using different phenotypes.ResultsThe HW phenotype demonstrated the strongest association with all metabolic components. The best phenotypic predictors were the forehead-to-rib circumference ratio + TG for the HDL level, age + TG for the LDL level, age + TG for SBP, and rib circumference + TG and neck circumference + TG for DBP. The associations between TG and TC or HDL were higher compared with those between WC and TC or HDL, whereas the associations between WC and SBP or DBP were higher compared with those between TG and SBP or DBP. Age was strongly associated with hypercholesterolemia, the HDL and LDL cholesterol levels, and SBP and had good predictive power, but not with respect to DBP.ConclusionsWe have determined that the HW phenotype is a useful indicator of metabolic abnormalities in Korean women; although HW had the strongest association with metabolic abnormalities, the best phenotype combination consisting of a single anthropometric index and the TG level may differ depending on the metabolic factors in question. Our findings provide insights into the detection of metabolic abnormalities in complementary and alternative medicine.

Association of peptic ulcer disease with obesity, nutritional components, and blood parameters in the Korean population

Objectives Peptic ulcer disease (PUD) is a common disorder, but whether an association exists between PUD and anthropometric indicators remains controversial. Furthermore, no studies on the association of PUD with anthropometric indices, blood parameters, and nutritional components have been reported. The aim of this study was to assess associations of anthropometrics, blood parameters, nutritional components, and lifestyle factors with PUD in the Korean population. Methods Data were collected from a nationally representative sample of the South Korean population using the Korea National Health and Nutrition Examination Survey. Logistic regression was used to examine associations of anthropometrics, blood parameters and nutritional components among patients with PUD. Results Age was the factor most strongly associated with PUD in women (p = <0.0001, odds ratio (OR) = 0.770 [0.683–0.869]) and men (p = <0.0001, OR = 0.715 [0.616–0.831]). In both crude and adjusted analyses, PUD was highly associated with weight (adjusted p = 0.0008, adjusted OR = 1.251 [95%CI: 1.098–1.426]), hip circumference (adjusted p = 0.005, adjusted OR = 1.198 [1.056–1.360]), and body mass index (adjusted p = 0.0001, adjusted OR = 1.303 [1.139–1.490]) in women and hip circumference (adjusted p = 0.0199, adjusted OR = 1.217 [1.031–1.435]) in men. PUD was significantly associated with intake of fiber (adjusted p = 0.0386, adjusted OR = 1.157 [1.008–1.328], vitamin B2 (adjusted p = 0.0477, adjusted OR = 1.155 [1.001–1.333]), sodium (adjusted p = 0.0154, adjusted OR = 1.191 [1.034–1.372]), calcium (adjusted p = 0.0079, adjusted OR = 1.243 [1.059–1.459]), and ash (adjusted p = 0.0468, adjusted OR = 1.152 [1.002–1.325] in women but not in men. None of the assessed blood parameters were associated with PUD in women, and only triglyceride level was associated with PUD in men (adjusted p = 0.0169, adjusted OR = 1.227 [1.037–1.451]). Discussion We found that obesity was associated with PUD in the Korean population; additionally, the association between nutritional components and PUD was greater in women than in men.