• Journal of agricultural medicine and community health
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• v.22 no.1
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• pp.1-18
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• 1997

This study was carried out to reveal the status of health examination among farmers and to attract more attention to the health care system for farmers. Ten pre-trained medical students interviewed the rural residents 18 years of age and older in eight villages which were randomly selected from a county near Taegu city in Korea, in August 1996. Finally 751 persons were interviewed of whom the percentages of male and female were 41.8%, 58.2% respectively. Among the subjects, 361(48.3%) were fully engaged in farming, 184(24.4%) were partly engaged, and the remaining 206(27.3%) were not engaged in farming at all. The overall prevalence of farmer's disease was 23.0% and there was no significant difference between the group of fully engaged in farming(23.3%) and the group of not-fully engaged(22.9%). But the prevalence of farmer's disease in female subjects(27.8%) was significantly higher than that in male(16.2%)(p<0.01). Among the 288 farmer engaged in spraying pesticide, 113(39.2%) had experienced one or more pesticide related symptoms during last one year, but only 18(15.9%) of them had visited medical facilities due to their symptoms. The experience of receiving education about pesticide was significantly correlated with the degree of wearing protectors during pesticide spraying(p<0.001). Among the 736 persons excluding non-respondents, 281(38.2%) received health examination during last one year ; 176(62.6%) of them received free health examination, and 105(37.4%) received charged one. Among the 533 persons 40 years age and older, only 124(23.3%) had received the 'health examination for the elderly' during last one year, which is provided for the 40 years age and older by Korea medical insurance corporation and medical insurance societies. Most of all beneficiaries of self-employed medical insurance thought the imposed contributions as very expensive(77.4%) or moderately expensive(13.2%). The great majority of farmers are exposed to various health risk factors including pesticide, high temperature, overwork etc. comparable to industrial workers. But farmers are excluded from the regular yearly worker's health examination because of not belonging to a company despite they pay relatively more medical insurance contributions compared with the industrial workers and the urban self-employed medical insureds. It is necessary to develop special health management program for farmers such as the special health examination for the industrial workers exposed harmful agents.

• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.85-109
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• 2018

Recommender system recommends the items expected to be purchased by a customer in the future according to his or her previous purchase behaviors. It has been served as a tool for realizing one-to-one personalization for an e-commerce service company. Traditional recommender systems, especially the recommender systems based on collaborative filtering (CF), which is the most popular recommendation algorithm in both academy and industry, are designed to generate the items list for recommendation by using 'overall rating' - a single criterion. However, it has critical limitations in understanding the customers' preferences in detail. Recently, to mitigate these limitations, some leading e-commerce companies have begun to get feedback from their customers in a form of 'multicritera ratings'. Multicriteria ratings enable the companies to understand their customers' preferences from the multidimensional viewpoints. Moreover, it is easy to handle and analyze the multidimensional ratings because they are quantitative. But, the recommendation using multicritera ratings also has limitation that it may omit detail information on a user's preference because it only considers three-to-five predetermined criteria in most cases. Under this background, this study proposes a novel hybrid recommendation system, which selectively uses the results from 'traditional CF' and 'CF using multicriteria ratings'. Our proposed system is based on the premise that some people have holistic preference scheme, whereas others have composite preference scheme. Thus, our system is designed to use traditional CF using overall rating for the users with holistic preference, and to use CF using multicriteria ratings for the users with composite preference. To validate the usefulness of the proposed system, we applied it to a real-world dataset regarding the recommendation for POI (point-of-interests). Providing personalized POI recommendation is getting more attentions as the popularity of the location-based services such as Yelp and Foursquare increases. The dataset was collected from university students via a Web-based online survey system. Using the survey system, we collected the overall ratings as well as the ratings for each criterion for 48 POIs that are located near K university in Seoul, South Korea. The criteria include 'food or taste', 'price' and 'service or mood'. As a result, we obtain 2,878 valid ratings from 112 users. Among 48 items, 38 items (80%) are used as training dataset, and the remaining 10 items (20%) are used as validation dataset. To examine the effectiveness of the proposed system (i.e. hybrid selective model), we compared its performance to the performances of two comparison models - the traditional CF and the CF with multicriteria ratings. The performances of recommender systems were evaluated by using two metrics - average MAE(mean absolute error) and precision-in-top-N. Precision-in-top-N represents the percentage of truly high overall ratings among those that the model predicted would be the N most relevant items for each user. The experimental system was developed using Microsoft Visual Basic for Applications (VBA). The experimental results showed that our proposed system (avg. MAE = 0.584) outperformed traditional CF (avg. MAE = 0.591) as well as multicriteria CF (avg. AVE = 0.608). We also found that multicriteria CF showed worse performance compared to traditional CF in our data set, which is contradictory to the results in the most previous studies. This result supports the premise of our study that people have two different types of preference schemes - holistic and composite. Besides MAE, the proposed system outperformed all the comparison models in precision-in-top-3, precision-in-top-5, and precision-in-top-7. The results from the paired samples t-test presented that our proposed system outperformed traditional CF with 10% statistical significance level, and multicriteria CF with 1% statistical significance level from the perspective of average MAE. The proposed system sheds light on how to understand and utilize user's preference schemes in recommender systems domain.