Random Forest Ensembles versus LSTM Networks in Applied Machine Learning: A Comparative Review of Healthcare, Finance and Energy Studies

1. V. Borisov, T. Leemann, K. Seßler, J. Haug, M. Pawelczyk, and G. Kasneci, "Deep neural networks and tabular data: A survey," IEEE Trans. Neural Netw. Learn. Syst., vol. 35, no. 6, pp. 7499-7519, 2024, doi: 10.1109/TNNLS.2022.3229161.
2. O. B. Sezer, M. U. Gudelek, and A. M. Ozbayoglu, "Financial time series forecasting with deep learning: A systematic literature review: 2005-2019," Appl. Soft Comput., vol. 90, Art. no. 106181, 2020, doi: 10.1016/j.asoc.2020.106181.
3. E. J. Topol, "High-performance medicine: The convergence of human and artificial intelligence," Nat. Med., vol. 25, no. 1, pp. 44-56, 2019, doi: 10.1038/s41591-018-0300-7.
4. P. Kumar and T. J. P. Antony, "Machine learning and deep learning for medical diagnosis: A modality-wise review of clinical records, biosignals and medical images," Mach. Learn., vol. 6, no. 5, pp. 425-429, 2026.
5. M. Saeidi, W. Karwowski, F. V. Farahani, K. Fiok, R. Taiar, P. A. Hancock, and A. Al-Juaid, "Neural decoding of EEG signals with machine learning: A systematic review," Brain Sci., vol. 11, no. 11, Art. no. 1525, 2021, doi: 10.3390/brainsci11111525.
6. P. Probst, M. N. Wright, and A.-L. Boulesteix, "Hyperparameters and tuning strategies for random forest," WIREs Data Mining Knowl. Discov., vol. 9, no. 3, Art. no. e1301, 2019, doi: 10.1002/widm.1301.
7. H. Hewamalage, C. Bergmeir, and K. Bandara, "Recurrent neural networks for time series forecasting: Current status and future directions," Int. J. Forecast., vol. 37, no. 1, pp. 388-427, 2021, doi: 10.1016/j.ijforecast.2020.06.008.
8. C. Jamunadevi, P. Ragupathy, P. Sritha, S. Pandikumar, and S. Deepa, "Performance analysis of random forest classifier in extracting features from the EEG signal," in Proc. Int. Conf. Adv. Comput. Technol. Appl. (ICACTA), Coimbatore, India, 2022, pp. 1-5, doi: 10.1109/ICACTA54488.2022.9753364.
9. C. Jamunadevi, N. Rajasekaran, S. Pandikumar, R. Raja, and K. P. Narendra Prasanth, "Energy performance forecasting in buildings using machine learning," in Proc. Int. Conf. Commun., Comput. Internet Things (IC3IoT), Chennai, India, 2024, pp. 1-6, doi: 10.1109/IC3IoT60841.2024.10550233.10. S. Pandikumar, S. B. Sethupandian, M. S. Saravanan, S. N. Prasad, and M. Arun, "Deep learning based long short-term memory recurrent neural network for stock price movement prediction," Indian J. Sci. Technol., vol. 15, no. 11, pp. 474-480, 2022, doi: 10.17485/IJST/v15i11.27.
11. S. Kapoor and A. Narayanan, "Leakage and the reproducibility crisis in machine-learning-based science," Patterns, vol. 4, no. 9, Art. no. 100804, 2023, doi: 10.1016/j.patter.2023.100804.
12. L. Grinsztajn, E. Oyallon, and G. Varoquaux, "Why do tree-based models still outperform deep learning on typical tabular data?" in Adv. Neural Inf. Process. Syst. (NeurIPS) Datasets and Benchmarks Track, 2022.
13. R. Shwartz-Ziv and A. Armon, "Tabular data: Deep learning is not all you need," Inf. Fusion, vol. 81, pp. 84-90, 2022, doi: 10.1016/j.inffus.2021.11.011.
14. S. Pandikumar, S. P. Kabilan, and L. Amalraj, "Green IT: A study and analysis of environmental impact of social networks and search engines," Int. J. Comput. Appl., vol. 60, no. 6, 2012.
15. E. Strubell, A. Ganesh, and A. McCallum, "Energy and policy considerations for deep learning in NLP," in Proc. 57th Annu. Meeting Assoc. Comput. Linguistics (ACL), 2019, pp. 3645-3650, doi: 10.18653/v1/P19-1355.
16. V. Cerqueira, L. Torgo, and I. Mozetič, "Evaluating time series forecasting models: An empirical study on performance estimation methods," Mach. Learn., vol. 109, no. 11, pp. 1997-2028, 2020, doi: 10.1007/s10994-020-05910-7.
17. S. Fathi, R. Srinivasan, A. Fenner, and S. Fathi, "Machine learning applications in urban building energy performance forecasting: A systematic review," Renew. Sustain. Energy Rev., vol. 133, Art. no. 110287, 2020, doi: 10.1016/j.rser.2020.110287.
18. A. Craik, Y. He, and J. L. Contreras-Vidal, "Deep learning for electroencephalogram (EEG) classification tasks: A review," J. Neural Eng., vol. 16, no. 3, Art. no. 031001, 2019, doi: 10.1088/1741-2552/ab0ab5.
19. Y. Roy, H. Banville, I. Albuquerque, A. Gramfort, T. H. Falk, and J. Faubert, "Deep learning-based electroencephalography analysis: A systematic review," J. Neural Eng., vol. 16, no. 5, Art. no. 051001, 2019, doi: 10.1088/1741-2552/ab260c.
20. T. Margaret Mary, V. S. Prakash, K. Nagarajan, and K. S. Divya, "Breast cancer analysis in R to prognosis and prediction cancer diagnosis based on cell features," AIP Conf. Proc., vol. 3175, no. 1, Art. no. 020074, 2025, doi: 10.1063/5.0260540.
21. K. B. S. Prakash, M. Sudharsan, G. Meenakshi, and T. Margaret Mary, "Engaging coronary heart disease prediction (Vein) through Feature U-Net improvement with ensemble learning based hybrid bagging and boosting techniques," in Commun. Comput. Inf. Sci., vol. 2427. Cham, Switzerland: Springer, 2025, pp. 348-374, doi: 10.1007/978-3-031-86299-1_25.
22. T. Margaret Mary, V. S. Prakash, K. S. Divya, and A. George, "Hybrid ML algorithms for learning disability forecast in school going children using Python in machine learning techniques," in Proc. 7th Int. Conf. Electron., Commun. Aerosp. Technol. (ICECA), 2023, pp. 1043-1049, doi: 10.1109/ICECA58529.2023.10395259.
23. T. Margaret Mary, K. Soumya, G. Ramanathan, and G. Clinton, "An assessment on classification in Python using data science," in Proc. 3rd Int. Conf. Inventive Res. Comput. Appl. (ICIRCA), 2021, pp. 551-555.
24. A. KH, P. Kumar, R. HM, G. Akshaya, G. V. Yadav, K. N. Hegde, and P. K. Sodhi, "ScanWise: Concentration-aware machine learning framework for cosmetic product toxicity assessment," ITM Web Conf., vol. 87, Art. no. 01014, 2026.
25. S. Rajaprakash, C. B. Basha, K. Manivanan, C. Karthikeyan, and P. Kumar, "A novel approach to ASD detection using intuitionistic fuzzy sets and graph convolutional networks," Discover Artif. Intell., vol. 6, no. 1, Art. no. 874, 2026.
26. C. J. Kelly, A. Karthikesalingam, M. Suleyman, G. Corrado, and D. King, "Key challenges for delivering clinical impact with artificial intelligence," BMC Med., vol. 17, Art. no. 195, 2019, doi: 10.1186/s12916-019-1426-2.
27. A. Rajkomar, J. Dean, and I. Kohane, "Machine learning in medicine," N. Engl. J. Med., vol. 380, no. 14, pp. 1347-1358, 2019, doi: 10.1056/NEJMra1814259.
28. W. Jiang, "Applications of deep learning in stock market prediction: Recent progress," Expert Syst. Appl., vol. 184, Art. no. 115537, 2021, doi: 10.1016/j.eswa.2021.115537.
29. M. M. Kumbure, C. Lohrmann, P. Luukka, and J. Porras, "Machine learning techniques and data for stock market forecasting: A literature review," Expert Syst. Appl., vol. 197, Art. no. 116659, 2022, doi: 10.1016/j.eswa.2022.116659.
30. C. Jamunadevi, S. Prasath, V. E. Sathishkumar, and S. Pandikumar, "Enhancing loan approval prediction through advanced machine learning models," in Proc. 2nd Int. Conf. Sustain. Comput. Smart Syst. (ICSCSS), 2024.
31. P. Kumar, "Fake job post prediction using machine learning algorithms," Int. J. Innov. Res. Technol., vol. 9, no. 3, pp. 286-290.
32. C. Jamunadevi, S. Pandikumar, N. Rajasekaran, G. S. Sabarish et al., "Enhancing IPL match outcome prediction with privacy-preserving using machine learning techniques," in Proc. 15th Int. Conf. Comput. Commun. Netw. Technol. (ICCCNT), 2024.
33. J. M. Johnson and T. M. Khoshgoftaar, "Survey on deep learning with class imbalance," J. Big Data, vol. 6, Art. no. 27, 2019, doi: 10.1186/s40537-019-0192-5.
34. M. Bourdeau, X. Q. Zhai, E. Nefzaoui, X. Guo, and P. Chatellier, "Modeling and forecasting building energy consumption: A review of data-driven techniques," Sustain. Cities Soc., vol. 48, Art. no. 101533, 2019, doi: 10.1016/j.scs.2019.101533.
35. A. Zeng, M. Chen, L. Zhang, and Q. Xu, "Are transformers effective for time series forecasting?" in Proc. AAAI Conf. Artif. Intell., vol. 37, no. 9, 2023, pp. 11121-11128, doi: 10.1609/aaai.v37i9.26317.
36. S. M. Lundberg et al., "From local explanations to global understanding with explainable AI for trees," Nat. Mach. Intell., vol. 2, no. 1, pp. 56-67, 2020, doi: 10.1038/s42256-019-0138-9.
37. C. Rudin, "Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead," Nat. Mach. Intell., vol. 1, no. 5, pp. 206-215, 2019, doi: 10.1038/s42256-019-0048-x.
38. P. Henderson, J. Hu, J. Romoff, E. Brunskill, D. Jurafsky, and J. Pineau, "Towards the systematic reporting of the energy and carbon footprints of machine learning," J. Mach. Learn. Res., vol. 21, no. 248, pp. 1-43, 2020.
39. A. Lacoste, A. Luccioni, V. Schmidt, and T. Dandres, "Quantifying the carbon emissions of machine learning," arXiv:1910.09700, 2019.
40. C.-J. Wu et al., "Sustainable AI: Environmental implications, challenges and opportunities," in Proc. Mach. Learn. Syst. (MLSys), vol. 4, 2022, pp. 795-813.
41. D. Patterson et al., "The carbon footprint of machine learning training will plateau, then shrink," Computer, vol. 55, no. 7, pp. 18-28, 2022, doi: 10.1109/MC.2022.3148714.