Applied Ensemble Machine Learning Framework for Data-Driven Decision Support Using Socioeconomic Data
DOI:
https://doi.org/10.55927/fjmr.v5i1.698Keywords:
Applied Machine Learning, Ensemble Learning, Decision Support Systems, Socioeconomic Data, Data-Driven AnalysisAbstract
This study presents an applied ensemble machine learning framework for data-driven decision support using socioeconomic and demographic data. The problem is formulated as a supervised regression task, where nonlinear relationships between input variables and outcome indicators are approximated using Random Forest and Gradient Boosting models. The proposed framework emphasizes robustness, interpretability, and practical applicability rather than algorithmic novelty. Experimental results demonstrate that ensemble models achieve stable predictive performance under heterogeneous data conditions. Feature importance analysis highlights the contribution of key socioeconomic factors, illustrating how ensemble learning can support system-level understanding and analytical decision making. A real-world socioeconomic dataset is employed as a case study to demonstrate the applicability of the proposed framework in applied computing and informatics contexts.
References
A. Géron, Hands-On Machine Learning with Scikit-Learn and TensorFlow. O’Reilly, 2019.
A. Natekin and A. Knoll, “Gradient boosting machines, a tutorial,” Frontiers in Neurorobotics, vol. 7, 2013.
A. Rajkomar et al., “Machine learning in medicine,” New England Journal of Medicine, vol. 380, no. 14, pp. 1347–1358, 2019.
Advances in Neural Information Processing Systems (NeurIPS), pp. 4765– 4774, 2017.
C. G. Victora et al., “Maternal and child undernutrition: consequences for adult health,” The Lancet, vol. 371, no. 9608, pp. 340–357, 2008.
C. Molnar, Interpretable Machine Learning. 2nd ed., 2022.
C. Sammut and G. I. Webb, Eds. Springer, 2011, pp. 312–320.
E. Topol, “High-performance medicine: The convergence of human and artificial intelligence,” Nature Medicine, vol. 25, no. 1, pp. 44–56, 2019.
G. Brown, “Ensemble learning,” in Encyclopedia of Machine Learning,
H. Kantz and T. Schreiber, Nonlinear Time Series Analysis. Cambridge University Press, 2004.
J. Beam and I. Kohane, “Big data and machine learning in health care,” JAMA, vol. 319, no. 13, pp. 1317–1318, 2018.
J. H. Friedman, “Greedy function approximation: A gradient boosting machine,” Annals of Statistics, vol. 29, no. 5, pp. 1189–1232, 2001.
J. H. Holland, Complexity: A Very Short Introduction. Oxford University Press, 2014.
L. Breiman, “Random forests,” Machine Learning, vol. 45, no. 1, pp. 5–32, 2001.
L. Rokach, “Ensemble-based classifiers,” Artificial Intelligence Review, vol. 33, no. 1–2, pp. 1–39, 2010.
M. de Onis and F. Branca, “Childhood stunting: A global perspective,” Maternal & Child Nutrition, vol. 12, pp. 12–26, 2016.
M. Kuhn and K. Johnson, Applied Predictive Modeling. Springer, 2013.
M. M. Islam et al., “Machine learning approaches for predicting child malnutrition,” BMC Medical Informatics and Decision Making, vol. 20, no. 1, 2020.
M. Newman, “Complex systems: A survey,” American Journal of Physics, vol. 79, no. 8, pp. 800–810, 2011.
M. Ruel and H. Alderman, “Nutrition-sensitive interventions and programmes,” The Lancet, vol. 382, no. 9891, pp. 536–551, 2013.
N. Rathi et al., “Predicting malnutrition using ensemble learning,” Journal of Infection and Public Health, vol. 14, no. 8, pp. 1046–1053, 2021.
P. Vega et al., “Prediction of childhood stunting using machine learning models,” Scientific Reports, vol. 11, no. 1, 2021.
R. E. Black et al., “Maternal and child undernutrition and overweight,” The Lancet, vol. 382, no. 9890, pp. 427–451, 2013.
R. M. May, “Simple mathematical models with very complicated dynamics,” Nature, vol. 261, pp. 459–467, 1976.
S. Lundberg and S.-I. Lee, “A unified approach to interpreting model predictions,”
S. Prendergast and J. Humphrey, “The stunting syndrome in developing countries,” Paediatrics and International Child Health, vol. 34, no. 4, pp. 250–265, 2014.
S. Strogatz, Nonlinear Dynamics and Chaos. Boulder, CO, USA: Westview Press, 2015.
T. Chen and C. Guestrin, “XGBoost: A scalable tree boosting system,” Proc. ACM SIGKDD, pp. 785–794, 2016.
T. G. Dietterich, “Ensemble methods in machine learning,” in Proc. Int. Workshop on Multiple Classifier Systems, pp. 1–15, 2000.
UNICEF, WHO, and World Bank, “Levels and trends in child malnutrition,” UNICEF, New York, NY, USA, 2021.
Y. Bar-Yam, Dynamics of Complex Systems. Westview Press, 2003.
Z.-H. Zhou, Ensemble Methods: Foundations and Algorithms. Boca Raton, FL, USA: CRC Press, 2012.
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