• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.111-124
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• 2018

This study aims to develop a classification model for predicting the occurrence of hyperlipidemia, one of the chronic diseases. Prior studies applying data mining techniques for predicting disease can be classified into a model design study for predicting cardiovascular disease and a study comparing disease prediction research results. In the case of foreign literatures, studies predicting cardiovascular disease were predominant in predicting disease using data mining techniques. Although domestic studies were not much different from those of foreign countries, studies focusing on hypertension and diabetes were mainly conducted. Since hypertension and diabetes as well as chronic diseases, hyperlipidemia, are also of high importance, this study selected hyperlipidemia as the disease to be analyzed. We also developed a model for predicting hyperlipidemia using SVM and meta learning algorithms, which are already known to have excellent predictive power. In order to achieve the purpose of this study, we used data set from Korea Health Panel 2012. The Korean Health Panel produces basic data on the level of health expenditure, health level and health behavior, and has conducted an annual survey since 2008. In this study, 1,088 patients with hyperlipidemia were randomly selected from the hospitalized, outpatient, emergency, and chronic disease data of the Korean Health Panel in 2012, and 1,088 nonpatients were also randomly extracted. A total of 2,176 people were selected for the study. Three methods were used to select input variables for predicting hyperlipidemia. First, stepwise method was performed using logistic regression. Among the 17 variables, the categorical variables(except for length of smoking) are expressed as dummy variables, which are assumed to be separate variables on the basis of the reference group, and these variables were analyzed. Six variables (age, BMI, education level, marital status, smoking status, gender) excluding income level and smoking period were selected based on significance level 0.1. Second, C4.5 as a decision tree algorithm is used. The significant input variables were age, smoking status, and education level. Finally, C4.5 as a decision tree algorithm is used. In SVM, the input variables selected by genetic algorithms consisted of 6 variables such as age, marital status, education level, economic activity, smoking period, and physical activity status, and the input variables selected by genetic algorithms in artificial neural network consist of 3 variables such as age, marital status, and education level. Based on the selected parameters, we compared SVM, meta learning algorithm and other prediction models for hyperlipidemia patients, and compared the classification performances using TP rate and precision. The main results of the analysis are as follows. First, the accuracy of the SVM was 88.4% and the accuracy of the artificial neural network was 86.7%. Second, the accuracy of classification models using the selected input variables through stepwise method was slightly higher than that of classification models using the whole variables. Third, the precision of artificial neural network was higher than that of SVM when only three variables as input variables were selected by decision trees. As a result of classification models based on the input variables selected through the genetic algorithm, classification accuracy of SVM was 88.5% and that of artificial neural network was 87.9%. Finally, this study indicated that stacking as the meta learning algorithm proposed in this study, has the best performance when it uses the predicted outputs of SVM and MLP as input variables of SVM, which is a meta classifier. The purpose of this study was to predict hyperlipidemia, one of the representative chronic diseases. To do this, we used SVM and meta-learning algorithms, which is known to have high accuracy. As a result, the accuracy of classification of hyperlipidemia in the stacking as a meta learner was higher than other meta-learning algorithms. However, the predictive performance of the meta-learning algorithm proposed in this study is the same as that of SVM with the best performance (88.6%) among the single models. The limitations of this study are as follows. First, various variable selection methods were tried, but most variables used in the study were categorical dummy variables. In the case with a large number of categorical variables, the results may be different if continuous variables are used because the model can be better suited to categorical variables such as decision trees than general models such as neural networks. Despite these limitations, this study has significance in predicting hyperlipidemia with hybrid models such as met learning algorithms which have not been studied previously. It can be said that the result of improving the model accuracy by applying various variable selection techniques is meaningful. In addition, it is expected that our proposed model will be effective for the prevention and management of hyperlipidemia.

• Journal of Chest Surgery
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• v.39 no.12 s.269
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• pp.879-890
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• 2006

Background: The dysfunction of multiple organs is found to be caused by reactive oxygen species as a major modulator of microvascular injury after hemorrhagic shock. Hemorrhagic shock, one of many causes inducing acute lung injury, is associated with increase in alveolocapillary permeability and characterized by edema, neutrophil infiltration, and hemorrhage in the interstitial and alveolar space. Aggressive and rapid fluid resuscitation potentially might increased the risk of pulmonary dysfunction by the interstitial edema. Therefore, in order to improve the pulmonary dysfunction induced by hemorrhagic shock, the present study was attempted to investigate how to reduce the inflammatory responses and edema in lung. Material and Method: Male Sprague-Dawley rats, weight 300 to 350 gm were anesthetized with ketamine(7 mg/kg) intramuscular Hemorrhagic Shock(HS) was induced by withdrawal of 3 mL/100 g over 10 min. through right jugular vein. Mean arterial pressure was then maintained at $35{\sim}40$ mmHg by further blood withdrawal. At 60 min. after HS, the shed blood and Ringer's solution or 5% albumin was infused to restore mean carotid arterial pressure over 80 mmHg. Rats were divided into three groups according to rectal temperature level($37^{\circ}C$[normothermia] vs $33^{\circ}C$[mild hypothermia]) and resuscitation fluid(lactate Ringer's solution vs 5% albumin solution). Group I consisted of rats with the normothermia and lactate Ringer's solution infusion. Group II consisted of rats with the systemic hypothermia and lactate Ringer's solution infusion. Group III consisted of rats with the systemic hypothermia and 5% albumin solution infusion. Hemodynamic parameters(heart rate, mean carotid arterial pressure), metabolism, and pulmonary tissue damage were observed for 4 hours. Result: In all experimental groups including 6 rats in group I, totally 26 rats were alive in 3rd stage. However, bleeding volume of group I in first stage was $3.2{\pm}0.5$ mL/100 g less than those of group II($3.9{\pm}0.8$ mL/100 g) and group III($4.1{\pm}0.7$ mL/100 g). Fluid volume infused in 2nd stage was $28.6{\pm}6.0$ mL(group I), $20.6{\pm}4.0$ mL(group II) and $14.7{\pm}2.7$ mL(group III), retrospectively in which there was statistically a significance between all groups(p<0.05). Plasma potassium level was markedly elevated in comparison with other groups(II and III), whereas glucose level was obviously reduced in 2nd stage of group I. Level of interleukine-8 in group I was obviously higher than that of group II or III(p<0.05). They were $1.834{\pm}437$ pg/mL(group I), $1,006{\pm}532$ pg/mL(group II), and $764{\pm}302$ pg/mL(group III), retrospectively. In histologic score, the score of group III($1.6{\pm}0.6$) was significantly lower than that of group I($2.8{\pm}1.2$)(p<0.05). Conclusion: In pressure-controlled hemorrhagic shock model, it is suggested that hypothermia might inhibit the direct damage of ischemic tissue through reduction of basic metabolic rate in shock state compared to normothermia. It seems that hypothermia should be benefit to recovery pulmonary function by reducing replaced fluid volume, inhibiting anti-inflammatory agent(IL-8) and leukocyte infiltration in state of ischemia-reperfusion injury. However, if is considered that other changes in pulmonary damage and inflammatory responses might induce by not only kinds of fluid solutions but also hypothermia, and that the detailed evaluation should be study.