Machine Learning Approaches to Predicting Pacemaker Battery Life

Abstract

Accurate prediction of pacemaker battery life is critical to timely generator replacement and patient safety. We evaluated three regression approaches: multilayer perceptron Neural Networks (NN), Random Forests (RF), and Linear Regression (LR), using 42 real‑world interrogation reports spanning single, dual, and triple‑chamber Medtronic devices. Key electrical parameters (battery voltage/current, lead impedance, capture thresholds, pacing percentages, etc.) were modelled. Performance was quantified with mean absolute error (MAE), mean squared error (MSE), and coefficient of determination (R²). NNs achieved the highest accuracy (R² ≈ 1.0; MAE < 0.1 months), RF provided robust results (R² ≈ 0.85), whereas LR exhibited limited predictive fidelity (R²  ≤  0.41). “Monte‑Carlo simulations (n = 1000)” and 95 % prediction intervals characterized predictive uncertainty; residual and Q‑Q analyses verified statistical assumptions. Our findings indicate that a data‑driven NN framework can reliably forecast remaining battery longevity, enabling proactive replacement scheduling and reducing unexpected generator depletion. The methodology is compatible with different manufacturers and suitable to integration within remote device follow‑up systems to enhance longitudinal cardiac care.