Machine learning models in health prevention and promotion and labor productivity: A co-word analysis


  • Sergio Arturo Dominguez Miranda Universidad Panamericana
  • Roman Rodriguez Aguilar Universidad panamericana



co-word analysis, research trends, bibliometrics, machine learning models, health prevention and promotion, labor productivity


Objective: The objective of this article is 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.

Methodology: 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 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 as well as thematic evolution.

Results: 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.

Conclusions: 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 focused on improvements in labor productivity through prevention and promotion of health.

Originality: The identification of the relationship between work productivity and health prevention and promotion through machine learning models is a relevant topic but little 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.


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Abdul Salam, M., Taha, S., & Ramadan, M. (2021). COVID-19 detection using federated machine learning. PLoS One, 16(6), e0252573. DOI:

Aguilar, C.A. (1999). Promoción de la salud para la prevención de las enfermedades crónico-degenerativas vinculadas con la alimentación y el estilo de vida. Salud Comunitaria y promoción de la salud. ICEPSS Editores.

Amarasingam, N., Salgadoe, A. S. A., Powell, K., Gonzalez, L. F., & Natarajan, S. (2022). A review of UAV platforms, sensors, and applications for monitoring of sugarcane crops. Remote Sensing Applications: Society and Environment, 26. 10.1016/j.rsase.2022.100712 DOI:

Antonovsky, A. (1979). Health, stress, and coping. New perspectives on mental and physical well-being, 12-37.

Arco-Canoles, D., del Carmen, O., Puenayan Portilla, Y. G., & Vaca Morales, L. V. (2019). Modelo de Promoción de la salud en el lugar de trabajo: una propuesta. Avances en Enfermería, 37(2), 227-236. DOI:

Baig, M. M., GholamHosseini, H., Moqeem, A. A., Mirza, F., & Lindén, M. (2017). A systematic review of wearable patient monitoring systems–current challenges and opportunities for clinical adoption. Journal of medical systems, 41(7), 1-9. DOI:

Baji, F., Azadeh, F., Parsaei-Mohammadi, P., & Parmah, S. (2018). Mapping intellectual structure of health literacy area based on co-word analysis in web of science database during the years 1993-2017. Health information management, 15(3), 139-145.

Bello-Chavolla, O. Y., Bahena-López, J. P., Vargas-Vázquez, A., Antonio-Villa, N. E., Márquez-Salinas, A., Fermín-Martínez, C. A., ... & Metabolic Syndrome Study Group. (2020). Clinical characterization of data-driven diabetes subgroups in Mexicans using a reproducible machine learning approach. BMJ Open Diabetes Research and Care, 8(1), e001550. DOI:

Beswick, D. M., Humphries, S. M., Balkissoon, C. D., Strand, M., Vladar, E. K., Lynch, D. A., & Taylor-Cousar, J. L. (2022). Impact of cystic fibrosis transmembrane conductance regulator therapy on chronic rhinosinusitis and health status: deep learning CT analysis and patient-reported outcomes. Annals of the American Thoracic Society, 19(1), 12-19. DOI:

Biundo, E., Pease, A., Segers, K., de Groote, M., d’Argent, T., & Schaetzen, E. D. (2020). The socio-economic impact of AI in healthcare. Deloitte & MedTech Europe.

Callon, M., Courtial, J. P., & Laville, F. (1991). Co-word analysis as a tool for describing the network of interactions between basic and technological research: The case of polymer chemsitry. Scientometrics, 22, 155-205. DOI:

Chaker, L., Falla, A., van der Lee, S.J., Muka, T., Imo, D., Jaspers, L., Colpani, V., Mendis, S., Chowdhury, R., Bramer, W. M., Pazoki, R., & Franco, O. H. (2015). The global impact of non-communicable diseases on macro-economic productivity: a systematic review. European Journal of Epidemiology, 30(5), 357-395. DOI:

Chapman, L. S. & Pelletier, K. R. (2004) Population Health Management as a Strategy for Creation of Optimal Healing Environments in Worksite and Corporate Settings. The journal of alternative and complementary medicine. 10(1), 127–140. DOI:

Cheng, B., & Wang, M. (2011). Co-occurrence analysis of domain knowledge in e-learning enabled workforce development. International Journal of Continuing Engineering Education and Life Long Learning, 21(1), 87-102. DOI:

Córdova-Villalobos, J.A., Barriguete-Meléndez, J.A., Lara-Esqueda, A., Barquera, S., Rosas-Peralta, M., Hernández-Ávila, M., León-May, M.A. & Aguilar-Salinas, C.A. (2008). Las enfermedades crónicas no transmisibles en México: sinopsis epidemiológica y prevención integral. Salud Pública de México, 50 (5): 419-427. Recuperado en 30 de marzo de 2022, de DOI:

Currie, G., & Hawk, K. E. (2021, March). Ethical and legal challenges of artificial intelligence in nuclear medicine. In Seminars in Nuclear Medicine (Vol. 51, No. 2, pp. 120-125). WB Saunders. DOI:

De la Hoz-Correa, A., Muñoz-Leiva, F., & Bakucz, M. (2018). Past themes and future trends in medical tourism research: A co-word analysis. Tourism management, 65, 200-211. DOI:

Domínguez-Miranda, S. A., & Rodríguez-Aguilar, R. (2022, June). Health 4.0, Prevention, and Health Promotion in Companies: A Systematic Literature Review. In International Conference on Computer Science and Health Engineering (pp. 217-245). Cham: Springer International Publishing. DOI:

El Naqa, I., & Murphy, M. J. (2015). What is machine learning?. Machine learning in radiation oncology. 3-11. Springer, Cham. DOI:

Fatima, M., & Pasha, M. (2017). Survey of machine learning algorithms for disease diagnostic. Journal of Intelligent Learning Systems and Applications, 9(01), 1. DOI:

Finkelstein, E. A., Sahasranaman, A., John, G., Haaland, B. A., Bilger, M., Sloan, R. A.; Khaing, E.E & Evenson, K. R. (2015). Design and baseline characteristics of participants in the Trial of Economic Incentives to Promote PA (TRIPPA): a randomized controlled trial of a six-month pedometer program with financial incentives. Contemporary clinical trials, 41, 238-247. DOI:

Golder, S. A., & Macy, M. W. (2011). Diurnal and seasonal mood vary with work, sleep, and daylength across diverse cultures. Science, 333(6051), 1878-1881. DOI:

González-Valiente, C. L., Costas, R., Noyons, E., Steinerová, J., & Šušol, J. (2021). Terminological (di) similarities between information management and knowledge management: a term co-occurrence analysis. Mobile Networks and Applications, 26(1), 336-346. DOI:

Haldar, S.K. & Mallik, G. (2010): Does human capital cause economic growth? A case study of India, International Journal of Economic Sciences and Applied Research. Kavala Institute of Technology, 3(1) 7-25.

Irizarry, R. A. (2019). Introduction to data science: Data analysis and prediction algorithms with R. CRC Press. DOI:

Jaspers, L., Colpani, V., Chaker, L., van der Lee, S.J., Muka, T., Imo, D., Mendis, S., Chowdhury, R., Bramer, W. M., Falla, A., Pazoki, R., & Franco, O. H. (2015). The global impact of non-communicable diseases on households and impoverishment: a systematic review. European Journal of Epidemiology, 30(3), 163-188. DOI:

Kjell, K., Johnsson, P., & Sikström, S. (2021). Freely generated word responses analyzed with artificial intelligence predict self-reported symptoms of depression, anxiety, and worry. Frontiers in Psychology, 12, 602581. DOI:

Koopmanschap, M. A., Rutten, F. F., van Ineveld, B. M., & Van Roijen, L. (1995). The friction cost method for measuring indirect costs of disease. Journal of health economics, 14(2), 171-189. DOI:

Kumar, Y., & Mahajan, M. (2020). Recent advancement of machine learning and deep learning in the field of healthcare system. Computational intelligence for machine learning and healthcare informatics, 1, 77. DOI:

Lee, J. S., Kang, M. A., & Lee, S. K. (2020). Effects of the e-Motivate4Change program on metabolic syndrome in young adults using health apps and wearable devices: Quasi-experimental study. Journal of medical Internet research, 22(7), e17031. DOI:

Leydesdorff, L., & Rafols, I. (2009). A global map of science based on the ISI subject categories. Journal of the American Society for Information Science and Technology, 60(2), 348-362. DOI:

Li, Y., Bai, C., & Reddy, C. K. (2016). A distributed ensemble approach for mining healthcare data under privacy constraints. Information sciences, 330, 245-259. DOI:

Lis, A. (2018). Keywords co-occurrence analysis of research on sustainable enterprise and sustainable organisation. Journal of Corporate Responsibility and Leadership, 5(2), 47-66.

Mahesh, B. (2020). Machine learning algorithms-a review. International Journal of Science and Research (IJSR).[Internet], 9(1), 381-386.

Mansyur, M. (2021). Occupational Health, Productivity and Evidence-Based Workplace Health Intervention. Acta Medica Philippina, 55(6). DOI:

Mayne, T. J., Howard, K., & Brandt-Rauf, P. W. (2004). Measuring and evaluating the effects of disease on workplace productivity. Journal of Occupational and Environmental Medicine, 46(6), S1-S2. DOI:

Mhasawade, V., Zhao, Y., & Chunara, R. (2021). Machine learning and algorithmic fairness in public and population health. Nature Machine Intelligence, 3(8), 659-666. DOI:

Nutbeam, D. & Muscat, D. M. (2021). Health promotion glossary 2021. Health Promotion International, 36(6), 1578-1598. DOI:

OPS, Organización Panamericana de la Salud. (2022). Curso de vida saludable. Accessed on October 6th, 2023, from:

Rangel-Baltazar, E., Cuevas-Nasu, L., Shamah-Levy, T., Rodríguez-Ramírez, S., Méndez-Gómez-Humarán, I., & Rivera, J. A. (2019). Association between high waist-to-height ratio and cardiovascular risk among adults sampled by the 2016 Half-Way National Health and Nutrition Survey in Mexico (ENSANUT MC 2016). Nutrients, 11(6), 1402. DOI:

Roberts Jr, J. M. (2000). Correspondence analysis of two-mode network data. Social Networks, 22(1), 65-72. DOI:

Rongen, A., Robroek, S. J., van Lenthe, F. J., & Burdorf, A. (2013). Workplace health promotion: a meta-analysis of effectiveness. American journal of preventive medicine, 44(4), 406-415. DOI:

Rozjabek, H., Fastenau, J., LaPrade, A., & Sternbach, N. (2020). Adult obesity and health-related quality of life, patient activation, work productivity, and weight loss behaviors in the United States. Diabetes, metabolic syndrome and obesity: targets and therapy, 13, 2049. DOI:

SAP. (2022). ¿Qué es Machine Learning?. Accessed on October 6th, 2023, from:

Schultz, T. P. (1997). Assessing the productive benefits of nutrition and health: An integrated human capital approach. Journal of Econometrics, 77(1), 141-158. DOI:

Shahbaz, M., Ali, S., Guergachi, A., Niazi, A., & Umer, A. (2019, July). Classification of Alzheimer's Disease using Machine Learning Techniques. In Data (pp. 296-303). DOI:

Silverio, A., Cavallo, P., De Rosa, R., & Galasso, G. (2019). Big health data and cardiovascular diseases: a challenge for research, an opportunity for clinical care. Frontiers in medicine, 6, 36. DOI:

Tompa, E., Dolinschi, R., & De Oliveira, C. (2006). Practice and potential of economic evaluation of workplace-based interventions for occupational health and safety. Journal of Occupational Rehabilitation, 16(3), 367-392. DOI:

Torii, M., Fan, J. W., Yang, W. L., Lee, T., Wiley, M. T., Zisook, D. S., & Huang, Y. (2015). Risk factor detection for heart disease by applying text analytics in electronic medical records. Journal of biomedical informatics, 58, S164-S170. DOI:

T-Systems. (2016). Big data y salud: Predicción de enfermedades. Accessed on October 6th, 2023 from:

Wagner, G.R (2014). Can Predictive Analytics Help Reduce Workplace Risk? Accessed on October 6th, 2023 from:

WHO, World Health Organization. (2021a). Non Communicable diseases.,of%20all%20premature%20NCD%20deaths.

Yang, Y., & Wu, L. (2021). Machine learning approaches to the unit commitment problem: Current trends, emerging challenges, and new strategies. The Electricity Journal, 34(1), 106889. DOI:

Zhang, H., Jiang, Y., Nguyen, H. D., Poo, D. C. C., & Wang, W. (2017). The effect of a smartphone-based coronary heart disease prevention (SBCHDP) programme on awareness and knowledge of CHD, stress, and cardiac-related lifestyle behaviours among the working population in Singapore: a pilot randomized controlled trial. Health and quality of life outcomes, 15(1), 1-13. DOI:

Zhang, W., Bansback, N., & Anis, A. H. (2011). Measuring and valuing productivity loss due to poor health: A critical review. Social science & medicine, 72(2), 185-192. DOI:




How 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.