[1] Emmanuel, T., Maupong, T., Mpoeleng, D., et al. "A Survey on Missing Data in Machine Learning." Journal of Big Data, vol. 8, no.

140, 2021.

[2] Palanivinayagam, A., and R. Damaevi?ius. "Effective Handling of Missing Values in Datasets for Classification Using Machine Learning Methods." Information, vol. 14, no. 2, 2023.

[3] Pudjihartono, N., Fadason, T., Kempa-Liehr, A. W., and J. M. O'Sullivan. "A Review of Feature Selection Methods for Machine Learning-Based Disease Risk Prediction." Frontiers in Bioinformatics, vol. 2, 2022.

[4] "Discover Feature Engineering: How to Engineer Features and How to Get Good at It." Machine Learning Mastery, Accessed 26 May

2024.

[5] Couronne, R., Probst, P., and A. L. Boulesteix. "Random Forest versus Logistic Regression: A Large-Scale Benchmark Experiment."

BMC Bioinformatics, vol. 19, no. 270, 2018.

[6] Shao, Z., Ahmad, M. N., and A. Javed. "Comparison of Random Forest and XGBoost Classifiers Using Integrated Optical and SAR Features for Mapping Urban Impervious Surface." Remote Sensing, vol. 16, no. 4, 2024.

[7] Hong, W., Zhou, X., Jin, S., Lu, Y., Pan, J., Lin, Q., Yang, S., et al. "A Comparison of XGBoost, Random Forest, and Nomograph for the

Prediction of Disease Severity in Patients With COVID-19 Pneumonia: Implications of Cytokine and Immune Cell Profile." Frontiers in

Cellular and Infection Microbiology, vol. 12, 2022.

[8] Zhang, Y., Liu, J., and W. Shen. "A Review of Ensemble Learning Algorithms Used in Remote Sensing Applications." Applied Sciences,

vol. 12, no. 17, 2022.