• Journal of Preventive Medicine and Public Health
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• 제40권3호
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• pp.233-238
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• 2007

Objectives : We aimed to assess the relationship between long-term exposure to air pollution and lung cancer in the Republic of Korea. Methods : Using the Annual Report of Ambient Air Quality in Korea, Annual Report of National Cancer Registration, and Annual Report on the Cause of Death Statistics, we calculated the standardized mortality ratio (SMR) and standardized incidence ratio (SIR) of lung cancer for both sexes in 74 areas from 7 Korean metropolitan cities. We performed random intercept, Poisson regression using empirical Bayes method. Results : Both SMRs and SIRs in the 7 metropolitan cities were higher in women than in men. Mean SIRs were 99.0 for males and 107.0 for females. The association between $PM_{10}$ and lung cancer risk differed according to gender. $PM_{10}$ was not associated with the risk of lung cancer in males, but both incidence and mortality of lung cancer were positively associated with $PM_{10}$ in females. The estimated percentage increases in the rate of female lung cancer mortality and incidence were 27% and 65% at the highest $PM_{10}$ category $({\geq}70\;{\mu}g/m^3)$, compared to the referent category $({\geq}50\;{\mu}g/m^3)$. Conclusions : Long-term exposure to $PM_{10}$ was significantly associated with female lung cancer incidence in 7 Korean metropolitan cities. Further study is undergoing to estimate the relative risk of $PM_{10}$ using multi-level analysis for controlling individual and regional confounders such as smoking and socioeconomic position.

• 한국IT서비스학회지
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• 제16권3호
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• pp.167-183
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• 2017

The rapid growth of information technology and mobile service platforms, i.e., internet, google, and facebook, etc. has led the abundance of data. Due to this environment, the world is now facing a revolution in the process that data is searched, collected, stored, and shared. Abundance of data gives us several opportunities to knowledge discovery and data mining techniques. In recent years, data mining methods as a solution to discovery and extraction of available knowledge in database has been more popular in e-commerce service fields such as, in particular, movie recommendation. However, most of the classification approaches for predicting the movie popularity have used only several types of information of the movie such as actor, director, rating score, language and countries etc. In this study, we propose a classification-based support vector machine (SVM) model for predicting the movie popularity based on movie's genre data and social network data. Social network analysis (SNA) is used for improving the classification accuracy. This study builds the movies' network (one mode network) based on initial data which is a two mode network as user-to-movie network. For the proposed method we computed degree centrality, betweenness centrality, closeness centrality, and eigenvector centrality as centrality measures in movie's network. Those four centrality values and movies' genre data were used to classify the movie popularity in this study. The logistic regression, neural network, $na{\ddot{i}}ve$ Bayes classifier, and decision tree as benchmarking models for movie popularity classification were also used for comparison with the performance of our proposed model. To assess the classifier's performance accuracy this study used MovieLens data as an open database. Our empirical results indicate that our proposed model with movie's genre and centrality data has by approximately 0% higher accuracy than other classification models with only movie's genre data. The implications of our results show that our proposed model can be used for improving movie popularity classification accuracy.