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

DOI

https://www.doi.org/10.59256/ijrtmr.20260606004

Published In

Volume 6, Issue 6 (2026)

Published Online

November-December 2026

Pages

24-31

Article Type

Original Article

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Random forest ensembles and long short-term memory (LSTM) networks are two of the most widely used model families in applied machine learning. Researchers in healthcare, finance and energy routinely choose between them, often with little guidance beyond habit or the popularity of deep learning. The choice matters because the two families make different assumptions about data, need different amounts of training data and tuning, and differ in interpretability and computational cost. Comparative evidence exists but is scattered across domain journals, and many applied studies compare a new model against weak baselines, which makes their conclusions hard to transfer. This review compares the two families across three application areas: clinical records and physiological signals, financial and commercial prediction, and building energy forecasting. It also examines four cross-cutting questions that decide which family is appropriate, namely the size and structure of the data, the soundness of the evaluation protocol, the need for explanation, and the energy and carbon cost of training and inference. The evidence shows that tree ensembles remain the stronger default on tabular and feature-engineered data such as EEG band powers, clinical measurements, loan applications and building attributes. LSTM networks are justified for long, regular sequences with enough history, as in high-frequency price and load series, but their reported gains often shrink under leakage-free, time-aware validation. Interpretability and energy use favour ensembles in most of the reviewed settings. Future studies should report strong ensemble baselines, use time-respecting validation, and state the energy cost of each model so that accuracy gains can be weighed against their price