HeartPredict algorithm: Machine intelligence for the early detection of heart failure

Heart failure (HF) is among the leading causes of death. Its prevalence is increasing dramatically causing considerable healthcare costs as well. Remote patient monitoring (RPM) is one of the solutions to enhance patient well-being. Taking advantage of new advances in artificial intelligence and RPM digital platforms, we propose HeartPredict: a novel machine learning algorithm for early detection of HF episodes and associated unplanned hospitalizations. The algorithm relies on a telemonitoring dataset from the largest European clinical study on HF. It uses balanced random forests on significant features extracted from patient weight time series, symptoms and socio-demographics. We benchmark HeartPredict with rules from medical guidelines and state of the art machine learning models. HeartPredict has better performance in terms of sensitivity (72% vs. guidelines: 53%) and specificity (94% vs. guidelines: 84%), with AUROC = 0.8. In addition, we introduce precocity as a new criterion to evaluate the ability of the algorithm to detect early HF risks, and therefore, enabling proactive medical actions. Finally, we show that HeartPredict prediction scores are consistent with HF risk levels, thereby limiting the risk of non-detection for patients.