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Safety-critical systems, such as implantable medical devices, are susceptible to soft errors—transient malfunctions that can lead to catastrophic failure. Traditional mitigation strategies like hardware redundancy often impose prohibitive costs and complexity for resource-constrained devices like pacemakers. This research proposes an advanced machine learning ensemble framework to predict and mitigate soft errors in real-time. A novel Feature Mimicry approach was employed to map biological heart data to technical pacemaker telemetry, reimagining features like Cholesterol as Internal Resistance and Blood Pressure as Battery Voltage. By evaluating both Bagging (Random Forest) and Boosting architectures (AdaBoost, XGBoost, LightGBM), the study compensates for the limitations of individual learners. Results demonstrate that boosting algorithms achieved a superior accuracy of 99.6% and a precision of 92.8% in predicting soft error states. The implementation of early stopping and loss convergence monitoring ensured the model remained robust against overfitting, establishing a high-precision, "zero-miss" diagnostic safety net for patients.