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Policy polls, which investigate the degree of support that the policy has for policy implementation, play an important role in making decisions. As the number of Internet users increases, the public is actively commenting on their policy news stories. Current policy polls tend to rely heavily on phone and offline surveys. Collecting and analyzing policy articles is useful in policy surveys. In this study, we propose a method of analyzing comments using deep learning technology showing outstanding performance in various fields. In particular, we designed various models based on the recurrent neural network (RNN) which is suitable for sequential data and compared the performance with the support vector machine (SVM), which is a traditional machine learning model. For all test sets, the SVM model show an accuracy of 0.73 and the RNN model have an accuracy of 0.83.
Generally, it is said that Taewongun seized the power through his personal ability; the distinguished trick and conspiracy to access to the throne. But such an explanation is not only unsatisfactory but also inaccessible to the historical truth. In 1860s, two serious problems distressed this country, which it had never known before. One was the social change. The development of commercial monetary economy worsened the feudalistic contradiction which had been already festered; the peasants rapidly got into impoverishment by the severe exploitation of the ruling class. The public officials of the feudalistic dynasty became deeply corrupt while the peasants began to upheave extremely suffered from heavy tax. Another problem was the invasion of the Western culture and powers; the Catholic permeated this country and agitate the stagnant water of feudalism while some armed fleets of the Western powers haunting its coast for the purpose of colonization. Under such an uneasy circumstance the feudalistic dynasty began to falter. Naturally a new demand burgeoned in the ruling class; a stout dictator who was powerful enough to keep the old system from falling was keenly demanded by the feudalistic ruling class. Taewongun's dramatic appearance on historical stage completely accords with the timely demand to overcome the danger coming both from inside and outside. Therefore, what caused Taewongun to seize the power would be the crucial situation Korea faced at the time.
Metabolic syndrome is closely associated with cardiovascular disease, there is increasing attentions in prevention of metabolic syndrome through prediction. The aim of this study was to systematically review the literature by collecting, analyzing, and synthesizing articles of predicting metabolic syndrome in Koreans. For systemic review, data search was conducted on Global journals Pubmed, WoS and domestic journals DBPia, KISS published in 2011-2020 year. Three keyword ‘Metabolic syndrome’, ‘predict’, and ‘korea’ were used for searching under AND condition. Total 560 articles were searched and the final 22 articles were selected according to the data selection criteria. The most useful variable was WHtR(AUC=0.897), most frequently used analysis method was logistic regression(63.6%), and most accurate analysis method was XGBOOST(AUC=0.879) for predicting metabolic syndrome. Prediction accuracy was slightly improved when sasang constitution types was used. Based on the results of this study, it is believed that various large-scale longitudinal studies for the prediction and management of the Metabolic syndrome in Korean should be followed in the future. 대사증후군은 심혈관질환과 밀접한 연관성을 가지며, 최근 대사증후군의 예측을 통한 예방에 관심이 증가하고 있다. 본 연구의 목적은 최근 한국인을 대상으로 한 대사증후군의 발병을 예측하는 논문을 수집, 분석, 종합하여 체계적 문헌고찰을 위한 것이다. 체계적 문헌고찰을 위해 자료검색은 Pubmed, WOS의 해외DB와 DBPia, KISS의 국내DB에서 검색하였으며, ‘Metabolic Syndrome’, ‘predict’, ‘Korea’ 세개의 키워드를 AND 조건으로 2011~2020년에 게재된 논문을 대상으로 검색하였다. 총 560편의 논문이 검색되었고 자료선정기준에 따라 최종 22편의 논문이 선별되었다. 대사증후군 예측에 가장 활용도가 높은 변수는 WHtR(AUC=0.897)이고, 가장 많이 사용된 분석방법은 로지스틱 회귀분석(63.6%), 가장 높은 정확도를 보이는 분석방법은 XGBOOST(AUC=0.879)였다. 또한 한의학적 체질 분류를 적용하는 경우 예측 정확도가 약간 향상되었다. 본 연구 결과를 토대로 한국인의 최적의 대사증후군 예측과 관리를 위한 대규모의 지속적 연구가 수반되어야 할 것으로 생각된다.