IoT-Enhanced Disease Prediction: A Machine Learning Approach to Climate-Health Interactions

The intersection of climate change and human health has increased the need for new solutions to predict and mitigate infectious diseases. This study explores an Internet of Things-enabled, machine learning-based framework to model and analyze the interactions between climate change and health outcomes. IoT sensors gather real-time environmental data, which is integrated with weather patterns and disease history. Advanced machine-learning techniques are then applied to detect and evaluate potential disease outbreaks in specific regions. This approach demonstrates the potential, accuracy, and potential of early warning, providing a valuable tool for governments to deploy health services promptly. This research advances understanding of the health-security equation and provides ideas and technologies to improve global health in the face of climate change.