Towards Effective Classification of Intrusion in Internet of Medical Things using Random Forest Classifier for Sustainable Health Care

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The random forest classifier achieved superior performance in detecting intrusions after optimizing model parameters.
Addressing data imbalance and feature selection significantly improved the model's ability to minimize false alarms.
A three-step methodology was employed, incorporating dataset analysis, feature handling, and model evaluation scenarios.
The study utilized the IoMT Attack Testbed dataset to test and refine the random forest classifier's performance.

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This study demonstrates improved intrusion detection capabilities in IoMT, suggesting optimized machine learning approaches.

Jimoh et al. (2025) studied this question.

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