Enhancing Healthcare Predictions: An Ensemble Approach to Integrating Multiple IoT Data Sources

The integration of Internet of Things (IoT) devices in healthcare has led to an abundance of patient data from diverse sources [1]. This study explores the application of ensemble methods to leverage multiple IoT data sources for health predictions. We propose a hybrid ensemble framework that combines data from wearable devices, smart home sensors, and clinical IoT equipment. Our approach utilizes a combination of traditional machine learning models and deep learn<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>ing techniques to create a predictive model [2]. The study was conducted on a dataset of 10,000 patients over a 12-month period, focusing on predicting the onset of chronic conditions such as diabetes and cardiovascular diseases. Results demonstrate that our hybrid ensemble method achieves a statistically significant improvement in prediction accuracy compared to single-source models, with an F1 score of 0.89 (95% CI: 0.87-0.91, p < 0.001). We also introduce a new interpretability metric for ensemble models and demonstrate a 30% improvement in model explainability. These findings suggest that our approach can enhance the reliability and interpretability of health predictions in IoT-enabled healthcare systems, potentially leading to more timely interventions and improved patient outcomes [3].