Investigación científica sobre regresión logística binaria: un análisis bibliométrico usando datos de Scopus (1974-2024)
DOI:
https://doi.org/10.15517/ye7cpy41Palabras clave:
Regresión logística binaria, Análisis bibliométrico, Tendencias de investigación, Impacto de la investigación global, Software R, VOSviewerResumen
Este estudio lleva a cabo un análisis bibliométrico exhaustivo de la investigación sobre regresión logística binaria entre 1974 y 2024. Examina 15,409 documentos escritos por 77,557 investigadores de 137 países y publicados en 4,669 fuentes diferentes. Los resultados revelan una tasa de crecimiento anual fuerte y sostenida de las publicaciones, con un notable aumento después de 2010, y un cambio global hacia aplicaciones interdisciplinarias. China lidera el volumen de publicaciones (10,553 artículos), mientras que el Reino Unido y los Países Bajos muestran el mayor impacto en cuanto a citas (aproximadamente 23.5 citas por artículo). Etiopía emerge como un contribuyente notable del Sur Global, con más de 8,000 publicaciones. A nivel de revistas, PLOS One y BMC Public Health destacan como las publicaciones más prolíficas, mientras que los trabajos de Hosmer y Lemeshow siguen siendo las referencias más influyentes en el campo. El mapeo temático destaca los grupos en salud pública, epidemiología e inteligencia artificial, lo que subraya el papel central de la regresión logística binaria tanto en el ámbito metodológico como en el aplicado. Estos hallazgos ofrecen una visión panorámica del campo, destacando los principales contribuyentes, las tendencias y las oportunidades para futuras investigaciones. El análisis se realizó utilizando el software R-4.4.1 y VOSviewer 1.6.20.
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[1] B. O. Ahinkorah, Predictors of Modern Contraceptive Use Among Adolescent Girls and Young Women in Sub-Saharan Africa: A Mixed Effects Multilevel Analysis of Data from 29 Demographic and Health Surveys. Contraception and Reproductive Medicine 5(2020), no. 1, 32. DOI: 10.1186/s40834- 020-00138-1
[2] M. Ahsan, T. Y. Susanto, T. A. Virania, A. I. Jaya, Credit Card Fraud Detection using Linear Discriminant Analysis (LDA), Random Forest, and Binary Logistic Regression. Barekeng 16(2022), no. 4, 1337–1346. DOI: 10.30598/barekengvol16iss4pp1337-1346
[3] R. Aleixandre-Benavent et al., Bibliometría e indicadores de actividad científica (II). Indicadores de producción científica en pediatría. Acta Pediátrica Español 75(2017), no. 3-4, 44–50.
[4] Z. Y. Algamal, M. H. Lee, Applying Penalized Binary Logistic Regression with Correlation Based Elastic Net for Variables Selection. Journal of Modern Applied Statistical Methods 14(2015), no. 1, 168–179. DOI: 10.22237/jmasm/1430453640
[5] M. Alhajj et al., Bibliometric analysis and evaluation of the Journal of Prosthetic Dentistry from 1970 to 2019. The Journal of Prosthetic Dentistry 129(2023), no. 2, 323–340. doi: 10.1016/j.prosdent.2021.05.013
[6] A. M. Alharthi, M. H. Lee, Z. Y. Algamal, Weighted L1-Norm Logistic Regression for Gene Selection of Microarray Gene Expression Classification. International Journal on Advanced Science, Engineering and Information Technology 10(2020), no. 4, 1483–1488. DOI: 10.18517/ijaseit.10.4.10907
[7] E. M. Ali, M. M. Ahmed, S. S. Wulff, Detection of Critical Safety Events on Freeways in Clear and Rainy Weather Using SHRP2 Naturalistic Driving Data: Parametric and Non-Parametric Techniques. Safety Science 119(2019), 141–149. DOI: 10.1016/j.ssci.2019.01.007
[8] M. Aria, C. Cuccurullo, bibliometrix: An R-tool for comprehensive science mapping analysis. Journal of informetrics 11(2017), no. 4, 959–975. DOI: 10.1016/j.joi.2017.08.007
[9] A. Bhattacharya, S. Biswas, A. Mandal, Credit risk evaluation: a comprehensive study. Multimedia Tools and Applications 82(2022), 18217–18267. DOI: 10.1007/s11042-022-13952-3
[10] W. Deng, J. Wang, The effect of entrepreneurship education on the entrepreneurial intention of different college students: Gender, household registration, school type, and poverty status. Plos One 18(2023), no. 7, e0288825. DOI: 10.1371/journal.pone.0288825
[11] N. Donthu, S. Kumar, D. Mukherjee, W. M. Lim, How to conduct a bibliometric analysis: An overview and guidelines. Journal of Business Research 133(2021), 285–296. DOI: 10.1016/j.jbusres.2021.04.070
[12] O. Ellegaard, J. A. Wallin, The bibliometric analysis of scholarly production: How great is the impact? English. Scientometrics 105(2015), no. 3, 1809–1831. DOI: 10.1007/s11192-015-1645-z
[13] Y.-n. Gan, D.-d. Li, N. Robinson, J.-p. Liu, Practical guidance on bibliometric analysis and mapping knowledge domains methodology – A summary. European Journal of Integrative Medicine 56(2022), 102203. DOI: 10.1016/j.eujim.2022.102203
[14] C. García-Villar, J. García-Santos, Bibliometric indicators to evaluate scientific activity. Radiología (English Edition) 63(2021), no. 3, 228–235. DOI: 10.1016/j.rxeng.2021.01.002
[15] M. Gaviria-Marin, J. M. Merigo, S. Popa, Twenty years of the Journal of Knowledge Management: a bibliometric analysis. Journal of Knowledge Management 22(2018), no. 8, 1655–1687. DOI: 10.1108/JKM-10-2017-0497
[16] A. Gonzales-Aguilar, M.-J. Colmenero-Ruiz, F.-C. Paletta, L. Verlaet, Loet Leydesdorff: bibliometric analysis and mapping of his scientific production. Profesional de la información 32(2023), no. 7. DOI: 10.3145/epi.2023.dic.09
[17] E. Herrera-Viedma, M. A. Martinez, M. Herrera, Bibliometric tools for discovering information in database. 29th International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems. Springer. 2016, 193–203. DOI: /10.1007/978-3-319-42007-3 17
[18] K. Ingale, R. Paluri, L. Bonfigt, Financial literacy and financial behaviour: a bibliometric analysis. Review of Behavioral Finance 14(2022), no. 1, 130–154. DOI: 10.1108/RBF-06-2020-0141
[19] N. Jayaratne, R. Zwahlen, The Evolution of Dental Journals from 2003 to 2012: A Bibliometric Analysis. PloS One 10(2015), no. 3, e0119503. DOI: 10.1371/journal.pone.0119503
[20] M. Khan et al., Value of special issues in the Journal of Business Research: A bibliometric analysis. Journal of Business Research 125(2021), 295–313. DOI: 10.1016/j.jbusres.2020.12.015
[21] S. Kim et al., What Predicts People’s Belief in COVID-19 Misinformation? A Retrospective Study Using a Nationwide Online Survey Among Adults Residing in the United States. BMC Public Health 22(2022), no. 1, 2114. DOI: 10.1186/s12889-022-14431-y
[22] Á. Kocsis, G. Molnár, Factors influencing academic performance and dropout rates in higher education. Oxford Review of Education 51(2024), no. 3, 414–432. DOI: 10.1080/03054985.2024.2316616
[23] B. W. Koo, S. Guhathakurta, N. Botchwey, How Are Neighborhood and Street-Level Walkability Factors Associated with Walking Behaviors? A Big Data Approach Using Street View Images. Environment and Behavior 54(2022), no. 1, 211–241. DOI: 10.1177/00139165211014609
[24] S. Kumar, V. Gota, Logistic regression in cancer research: A narrative review of the concept, analysis, and interpretation. Cancer Research, Statistics, and Treatment 6(2023), no. 4, 573–578. DOI: 10.4103/crst.crst 293 23
[25] M. K. Lazarides, I.-Z. Lazaridou, N. Papanas, Bibliometric analysis: bridging informatics with science. The international journal of lower extremity wounds 24(2025), no. 3, 515–517. DOI: 10.1177/15347346231153538
[26] C. Leal Iga, M. T. Cedilla Salazar, Regularización y mercado de suelo urbano en asentamientos irregulares: El Caso Cima de La Loma, Monterrey, México. Contexto 15(2021), no. 23, 89–106. DOI: 10.29105/contexto15.23-346
[27] J. Li, C. Xu, B. Feng, H. Zhao, Credit Risk Prediction Model for Listed Companies Based on CNN-LSTM and Attention Mechanism. Electronics 12(2023), 1643. DOI: 10.3390/electronics12071643
[28] Y. Li, F. Lu, Y. Yin, Applying Logistic LASSO Regression for the Diagnosis of Atypical Crohn’s Disease. Scientific Reports 12(2022), no. 1, 11340. DOI: 10.1038/s41598-022-15609-5
[29] W. M. Lim, S. Kumar, Guidelines for interpreting the results of bibliometric analysis: A sensemaking approach. Global Business and Organizational Excellence 43(2024), no. 2, 17–26. DOI: 10.1002/joe.22229
[30] L. Lombardo, P. M. Mai, Presenting Logistic Regression-Based Landslide Susceptibility Results. Engineering Geology 244(2018), 14–24. DOI: 10.1016/j.enggeo.2018.07.019
[31] C. D. Lopez et al., Using Machine Learning Methods to Predict Nonhome Discharge After Elective Total Shoulder Arthroplasty. JSES International 5(2021), no. 4, 692–698. DOI: 10.1016/j.jseint.2021.02.011
[32] J. López Guauque, A. Gil-Lafuente, Fifty years of fuzzy research: A bibliometric analysis and a long-term comparative overview. Journal of Intelligent & Fuzzy Systems 38(2020), no. 5, 5413–5425. DOI: 10.3233/JIFS-179634
[33] T. Luan et al., Dynamic Risk Analysis of Flammable Liquid Road Tanker Based on Fuzzy Bayesian Network. Process Safety Progress 42(2023), no. 4, 737–751. DOI: 10.1002/prs.12508
[34] J. Ma et al., Poor Handling of Continuous Predictors in Clinical Prediction Models Using Logistic Regression: A Systematic Review. Journal of Clinical Epidemiology 161(2023), 140–151. DOI: 10.1016/j.jclinepi.2023.07.017
[35] Q. Ma, Recent applications and perspectives of logistic regression modelling in healthcare. Theoretical and Natural Science 36(2024), 185–190. DOI: 10.54254/2753-8818/36/20240614
[36] C. A. Magee, P. C. L. Heaven, Big-Five Personality Factors, Obesity and 2- Year Weight Gain in Australian Adults. Journal of Research in Personality 45(2011), no. 3, 332–335. DOI: 10.1016/j.jrp.2011.02.009
[37] A. T. Manjatika, J. G. Davimes, P. Mazengenya, Estimation of sex using dimensions
around the metatarsal diaphyseal nutrient foramen: Application of discriminant function analysis and logistic regression models. Legal Medicine 68(2024), 102417. doi: 10.1016/j.legalmed.2024.102417
[38] E. O. Oyerogba, Forensic auditing mechanism and fraud detection: the case of Nigerian public sector. Journal of Accounting in Emerging Economies 11(2021), no. 5, 752–775. doi: 10.1108/JAEE-04-2020-0072
[39] M. J. Page et al., The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ 372(2021). DOI: 10.1136/bmj.n71
[40] N. R. Panda, J. K. Pati, J. N. Mohanty, R. Bhuyan, A review on logistic regression in medical research. National Journal of Community Medicine 13(2022), no. 04, 265–270.
[41] N. G. Polson, J. G. Scott, Data Augmentation for Non-Gaussian Regression Models Using Variance-Mean Mixtures. Biometrika 100(2013), no. 2, 459–471. DOI: 10.1093/biomet/ass081
[42] R. Pranckutė, Web of Science (WoS) and Scopus: The titans of bibliographic information in today’s academic world. Publications 9(2021), no. 1, 12. DOI: 10.3390/publications9010012
[43] Z. Runchi, L. Xue, W. Qin, An ensemble credit scoring model based on logistic regression with heterogeneous balancing and weighting effects. Expert Systems with Applications 212(2022), 118732. DOI: 10.1016/j.eswa.2022.118732
[44] A. Shaikh et al., Bibliometric analysis on the adoption of artificial intelligence applications in the e-health sector. Digital Health 9(2023), 205520762211492. DOI: 10.1177/20552076221149296
[45] Y. X. Song et al., Comparison of Logistic Regression and Machine Learning Methods for Predicting Postoperative Delirium in Elderly Patients: A Retrospective Study. CNS Neuroscience & Therapeutics 29(2023), no. 1, 158–167. DOI: 10.1111/cns.13991
[46] N. Suresh, B. Thomas, J. Joseph, Bibliometric Analysis and Visualization of Scientific Literature on Heart Disease Classification Using a Logistic Regression Model. Cureus 16(2024), no. 6, e63337. DOI: 10.7759/cureus.63337
[47] A. Syed, H. Bawazir, Recent trends in business financial risk – A bibliometric analysis. Cogent Economics & Finance 9(2021), no. 1, 1913877. DOI: 10.1080/ 23322039.2021.1913877
[48] D. M. Tom, Logistic Regression Teams Up With Artificial Intelligence To Beat Neural Network and Gradient Boosted Machine. 2021. DOI: 10.31224/osf.io/vz496
[49] A. Vaidya, Predictive and probabilistic approach using logistic regression: Application to prediction of loan approval. 2017 8th International Conference on Computing, Communication and Networking Technologies. 2017, 1–6. DOI: 10.1109/ICCCNT.2017.8203946
[50] S. Valenzuela, N. M. Somma, A. Scherman, A. Arriagada, Social Media in Latin America: Deepening or Bridging Gaps in Protest Participation? Online Information Review 40(2016), no. 5, 695–711. DOI: 10.1108/OIR-11-2015-0347
[51] M. G. Vaughn et al., Toward a Criminal Justice Epidemiology: Behavioral and Physical Health of Probationers and Parolees in the United States. Journal of Criminal Justice 40(2012), no. 3, 165–173. DOI: 10.1016/j.jcrimjus.2012.03.001
[52] J. Venema, D. Peula, J. Irurita, P. Mesejo, Employing deep learning for sex estimation of adult individuals using 2D images of the humerus. Neural Computing and Applications 35(2023), no. 8, 5987–5998. DOI: 10 . 1007 /s00521-022-07981-0
[53] L. Waltenberger et al., Lateral angle: A landmark-based method for the sex estimation in human cremated remains and application to an Austrian prehistoric sample. American journal of biological anthropology 184(2024), no. 1, e24874. DOI: 10.1002/ajpa.24874
[54] C. Wang, M. K. Lim, A. Lyons, Twenty years of the International Journal of Logistics Research and Applications: a bibliometric overview. International Journal of Logistics 22(2018), no. 3, 304–323. DOI: 10.1080/13675567.2018.1526262
[55] H. Wang et al., Bibliometric Analysis on the Progress of Chronic Heart Failure. Current Problems in Cardiology 47(2022), no. 9, 101213. DOI: 10.1016/j.cpcardiol.2022.101213
[56] A. Yildiz, Determining the factors for individual credit approval by applying logistic regression and hierarchical logistic regression. Int. Journal of Management Studies and Social Science Research 5(2023), no. 6, 58–67. DOI: 10.56293/ijmsssr.2023.4705
[57] J. Yoon, S. Lee, Spatio-Temporal Patterns in Pedestrian Crashes and Their Determining Factors: Application of a Space-Time Cube Analysis Model. Accident Analysis and Prevention 161(2021), 106291. DOI: 10.1016/j.aap.2021.106291
[58] Y. Zhou et al., Coronary Heart Disease and Depression or Anxiety: A Bibliometric Analysis. Frontiers in Psychology 12(2021), 669000. DOI: 10.3389/ fpsyg.2021.669000
[59] S. Zihan, S.-H. Sung, D.-M. Park, B.-K. Park, All-year dropout prediction modeling and analysis for university students. Applied Sciences 13(2023), no. 2, 1143. doi: 10.3390/app13021143
[60] M. Zuckerman et al., Biocultural perspectives on bioarchaeological and paleopathological evidence of past pandemics. American Journal of Biological Anthropology 182(2023), no. 4, 557–582. doi: 10.1002/ajpa.24647
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