BACKGROUND: To address critical security challenges in the Internet of Medical Things (IoMT), this study develops a feature selection framework to improve detection accuracy and computational efficiency in IoMT cybersecurity. By optimizing feature selection, the framework aims to enhance the security and operational integrity of real-time healthcare systems. METHOD: This study integrates Random Subset Feature Selection (RSFS) with Correlation Feature Selection (CFS) to create a novel feature selection framework tailored to IoMT datasets. The framework reduces data complexity and filters irrelevant features to improve model performance. It was tested on four IoMT datasets (WUSTL-EHMS-2020, TON-IoT, ICU-Dataset, ECU-IoHT) using machine learning models, including Random Forest, K-Nearest Neighbors, Support Vector Machine, Extreme Gradient Boosting, and a Deep Neural Network. RESULTS: The proposed framework achieved exceptional results: 99.82% accuracy on the TON-IoT dataset, 99.99% on the ICU-Dataset, 96.37% on the WUSTL-EHMS-2020 dataset, and 99.99% on the ECU-IoHT dataset. These results surpassed existing methods while utilizing a reduced feature set. The framework demonstrated significant improvements in detection accuracy and processing efficiency, addressing high-dimensional data challenges typical of IoMT environments. CONCLUSIONS: This study introduces a robust, scalable feature selection framework for IoMT cybersecurity, providing a practical solution to prevailing security gaps. By ensuring enhanced patient data protection and operational resilience, the framework holds potential for broad implementation in safeguarding critical IoMT infrastructures, advancing the field of secure healthcare systems.