Background: This study proposes a cardiovascular diseases (CVD) prediction model using machine learning (ML) algorithms based on the National Health Insurance Service-Health Screening datasets. Methods: We extracted 4699 patients aged over 45 as the CVD group, diagnosed according to the international classification of diseases system (I20–I25). In addition, 4699 random subjects without CVD diagnosis were enrolled as a non-CVD group. Both groups were matched by age and gender. Various ML algorithms were applied to perform CVD prediction; then, the performances of all the prediction models were compared. Results: The extreme gradient boosting, gradient boosting, and random forest algorithms exhibited the best average prediction accuracy (area under receiver operating characteristic curve (AUROC): 0.812, 0.812, and 0.811, respectively) among all algorithms validated in this study. Based on AUROC, the ML algorithms improved the CVD prediction performance, compared to previously proposed prediction models. Preexisting CVD history was the most important factor contributing to the accuracy of the prediction model, followed by total cholesterol, low-density lipoprotein cholesterol, waist-height ratio, and body mass index. Conclusions: Our results indicate that the proposed health screening dataset-based CVD prediction model using ML algorithms is readily applicable, produces validated results and outperforms the previous CVD prediction models.

Sustainability is a major contemporary issue that affects everyone. Many companies now produce an annual sustainability report, mainly intended for their stakeholders and the public, enumerating their goals and degrees of achievement regarding sustainable development. Although sustainability reports are an important resource to understand a company’s sustainability strategies and practices, the difficulty of extracting key information from dozens or hundreds of pages with sustainability and business jargon has highlighted the need for metrics to effectively measure the content of such reports. Accordingly, many researchers have attempted to analyze the concepts and messages from sustainability reports using various natural language processing (NLP) methods. In this study, we propose a novel approach that overcomes the shortcomings of previous studies. Using the sentence similarity method and sentiment analysis, the study clearly shows thematic practices and trends, as well as a significant difference in the balance of positive and negative information in the reports across companies. The results of sentiment analysis prove that the new approach of this study is very useful. It confirms that companies actively use the sustainability report to improve their positive image when they experience a crisis. It confirms that companies actively use the sustainability report to improve their positive image when they experience a crisis. The inferences gained from this method will not only help companies produce better reports that can be utilized effectively, but also provide researchers with ideas for further research. In the concluding section, we summarize the implications of our approach and discuss limitations and future research areas.

India has a longstanding reputation in the film industry, whereas South Korean films have only recently achieved notable success globally. Despite their significant positions in the global film market, there are very few studies that compare and analyze the competitive advantage of the two countries in the film industry. This paper adopts the ABCD model as a complementary framework to the two mainstream theories of strategic management (i.e., industry-based view and resource-based view) to analyze and compare the competitiveness of the industrial success of emerging countries. For the empirical test, this paper uses natural language processing methods to operationalize the theoretical model. After collecting text data from news articles in English related to the Korean and Indian film industries, this study analyzes how many keywords with regards to the 8 sub-factors of the ABCD model are mentioned in the articles using the document similarity measurement. The results reveal the different but complementary areas of strengths. India has higher competitiveness in the factor of Agility while Korea has higher competitiveness in Convergence. This study also highlights the areas for further development and potential partnership between the two countries by leveraging each other’s strengths.

Providing personalized product recommendations in offline retail stores, especially small-format offline retail businesses such as convenience stores, poses a great challenge. To address this issue, this study aimed to find a solution by shifting the perspective on recommendation methods and altering the target of recommendations. In this study, recommending products was defined as suggesting products that should be introduced and displayed within the store. This recommendation system proposes products that individual stores have not yet introduced but are anticipated to be purchased by customers. Building upon this, we developed a store-based collaborative filtering recommendation system. Furthermore, various rules and logic pertinent to store operations and business considerations for convenience stores were integrated to implement this recommendation system. The accuracy and effectiveness of the system were demonstrated through its application in actual convenience stores. Results from the pilot implementation of the system showed that 88% of the newly recommended products in individual stores were sold within a week, and the sales revenue was 1.75 times higher than the average sales of those products across the entire stores. Survey results on business owners’ satisfaction yielded a score of 4.2 out of 5, indicating a high level of contentment. This research holds significance in extending the scope of personalized recommendation studies from primarily online platforms to offline retail businesses such as convenience stores. The study also suggests avenues for future research to address some of the identified limitations.

Recently, e-commerce companies have been adopting and continuously enhancing personalized product recommendation systems, and there is active research in this field within academia. However, personalized product recommendations in offline retail stores have not yielded significant outcomes thus far. Especially for small-format offline retail businesses like convenience stores, where customer information might be limited, providing personalized product recommendations poses even greater challenges. To address this issue, this study aimed to find a solution by shifting the perspective on recommendation methods and altering the target of recommendations in existing personalized recommendation models. In this study, recommending products that customers need in an offline store was defined as suggesting products that should be introduced and displayed within the store. In other words, the recommendation focus shifted from individuals to the stores themselves. This recommendation system proposes products that individual stores have not yet introduced but are anticipated to be purchased by customers among the products managed by the headquarters. Building upon this, we devised a store-based product recommendation system. The widely used user-based collaborative filtering model, a common technique in existing recommendation systems, was adapted into a store-based collaborative filtering model. Furthermore, various rules and logic pertinent to store operations and business considerations for convenience stores were integrated to implement this store product recommendation system. The accuracy and effectiveness of the system were demonstrated through its application in actual convenience stores. Results from the pilot implementation of the system showed that 88% of the newly recommended products in individual stores were sold based on one week of sales data, and the sales revenue was 1.75 times higher than the average sales revenue of those products across the entire company. Survey results on business owners' satisfaction yielded a score of 4.2 out of 5, indicating a high level of contentment. Additional observed effects included the expansion of product range and reduction in order lead times. However, 12% of the total recommended SKUs did not sell within one week, and 34% of the SKUs did not meet the overall average sales quantity. This research holds significance in extending the scope of personalized recommendation studies from primarily online platforms to offline retail businesses like convenience stores. It provides tangible methodologies and outcomes that can be implemented in real-world settings through the construction and validation of an actual system. The study also suggests avenues for future research to address some of the identified limitations.