Mortality prediction using data from wearable activity trackers and individual characteristics: An explainable artificial intelligence approach

Mortality prediction plays a crucial role in healthcare by supporting informed decision-making for both public and personal health management. This study uses novel data sources such as wearable activity tracking devices, combined with explainable artificial intelligence methods, to enhance the accuracy and interpretability of mortality predictions. By using data from the UK Biobank—specifically wrist-worn accelerometer data, hospital records, and various demographic and lifestyle factors, and health-related factors—this research uncovers new insights into the predictors of mortality. Explainable artificial intelligence techniques are employed to make the models’ predictions more transparent and understandable, thereby improving their practical applications in healthcare decisions. Our analysis shows that random forest models achieve the highest prediction accuracy, with an area under the curve score of 0.78. Key predictors of mortality include age, physical activity levels captured by accelerometers, and other health and lifestyle factors. The study also identifies non-linear relationships between these predictors and mortality, and provides detailed explanations for individual-level predictions, offering deeper insights into risk factors.