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The study reviews relevant literature from the last 15 years indexed in Scopus and selects publications spanning articles, books, chapters, editorials, conference papers, and peer-reviewed reviews. Thematic mapping based on factor analysis and strategy diagrams identifies dominant algorithms, primary research approaches, frequent themes, and thematic evolution. Findings select 87 publications with an average annual growth rate of 23.25%, supporting future research directions.","Vol. 4, No. 1, 1-16. DOI: 10.47909/ijsmc.85  \nMachine learning models in health prevention and promotion and labor productivity: A co-word analysis  \nSergio Arturo Domínguez Miranda1, Roman Rodriguez-Aguilar2  \n1 Facultad de Ciencias Económicas y Empresariales, Universidad Panamericana, Augusto Rodin 498, 03920, Mexico City, México.  \n2 Facultad de Ciencias Económicas y Empresariales, Universidad Panamericana, Augusto Rodin 498, 03920, Mexico City, México.  \nEmail: [0246533@up.edu. mx](0246533@up.edu. mx)[ ](0246533@up.edu. mx)[Corresponding author](Corresponding author).  \nORIGINAL ARTICLE  \nABSTRACT  \nObjective. This article aims to carry out a co-word study on the application of machine learning models in health prevention and promotion and its effect on labor productivity.  \nDesign/Methodology/Approach. The analysis of the relevant literature on the proposed topic, identified in the last 15 years in Scopus, is considered. Articles, books, book chapters, editorials, conference papers, and reviews of refereed publications were considered. A thematic mapping analysis was performed using factor analysis and strategy diagrams to derive primary research approaches and identify frequent themes and thematic evolution.  \nResults/Discussion. The results of this study show the selection of 87 relevant publications with an average annual growth rate of 23.25% in related production. The main machine learning algorithms used, the main research approaches, and key authors derived from the analysis of thematic maps were identified.  \nConclusions. This study emphasizes the importance of using co-word analysis to understand trends in research on the impact of health prevention and promotion on labor productivity. The potential benefits of using machine learning models to address this issue are highlighted and anticipated to guide future research on improving labor productivity through prevention and health promotion.  \nOriginality/Value.. Identifying the relationship between work productivity and health prevention and promotion through machine learning models is relevant, but little has been analyzed in recent literature. The analysis of co-words allows us to establish the reference point of the state of the art in this regard and future trends.  \nKeywords: co-word analysis; research trends; bibliometrics; machine learning models; health prevention and promotion; labor productivity.  \nReceived: 13-12-2023. Accepted: 25-03-2024. Published: 06-04-2024  \nEditor: Adilson Luiz Pinto  \nHow to cite: Dominguez-Miranda, S. A., & Rodriguez-Aguilar, R. (2024). Machine learning models in health prevention and promotion and labor productivity: A co-word analysis. Iberoamerican Journal of Science Measurement and Communication; 4(1), 1-16. DOI: 10.47909/ijsmc.85  \nCopyright: © 2024 The author(s). This is an open access article distributed under the terms of the CC BY-NC 4.0 license which permits copying and redistributing the material in any medium or format, adapting, transforming, and building upon the material as long as the license terms are followed.  \nIberoamerican Journal of Science Measurement and Communication 1  \nDominguez-Miranda, Rodriguez-Aguilar ORIGINAL ARTICLE  \nINTRODUCTION  \nN on-communicable diseases (NCDs) stand  \nas the leading causes of death and a major public health concern globally (Córdova-Villalobos et al., 2008). The rise in NCDs is closely associated with an acute presentation of dyslipidemia, constituting one of the primary risk factors along with smoking, sedentary activities, improper nutrition, and genetic factors that contribute to the potentiation of diseases such as metabolic syndrome (diabetes, hypertension, and obesity), oncological, cardiological, and neurological conditions. Improving social conditions and the adoption of prevention and health promotion strategies such as diet quality, body weight, smoking cessation, and increasing physical activity can significantly alleviate the disease burden of these conditions.","cbCaipDVl2Goz1mf","https://ap.wps.com/l/cbCaipDVl2Goz1mf","pdf",1668817,1,16,"English","en",105,"# Introduction\n# Objective and Methodology\n## Data source and scope (Scopus, last 15 years)\n## Thematic mapping and strategy diagrams\n# Results and Discussion\n## Publication selection and growth rate\n## Algorithms, approaches, themes and authors\n# Conclusions\n## Implications for future research","[{\"question\":\"What is the objective of the co-word analysis in this study?\",\"answer\":\"The study carries out a co-word study on the application of machine learning models in health prevention and promotion and examines their effect on labor productivity.\"},{\"question\":\"What methodology is used to analyze the literature?\",\"answer\":\"Relevant publications from Scopus over the last 15 years are reviewed, and thematic mapping is performed using factor analysis and strategy diagrams to derive research approaches and identify themes and their evolution.\"},{\"question\":\"How many publications are selected and what is the growth rate?\",\"answer\":\"The results identify 87 relevant publications with an average annual growth rate of 23.25% in related research production.\"}]","Machine learning models in health prevention and promotion and labor productivity - 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