Big Data Analysis and Early Warning Algorithm for Chronic Diseases Based on the Industrial Internet of Things

ABSTRACT Aiming at the challenges of data imbalance and real‐time early warning in chronic disease monitoring, this paper proposes an improved big data analysis and early warning algorithm for chronic diseases based on industrial Internet of Things technology. In this paper, a fusion algorithm based on improved Stacking ensemble learning and LSTM timing prediction is proposed to solve the data imbalance and real‐time warning challenges in chronic disease monitoring in the IIoT environment. By introducing the Focal Loss function and Bayesian hyperparameter optimization, the recognition ability of minority samples and the overall performance of the model are significantly improved. On the diabetes dataset, AUC reached 0.912, F1 score increased by 32%, and MSE dropped to 0.24. Experiments show that the average accuracy of the algorithm in 5‐fold cross‐validation is 86.2%, which is better than the existing baseline method. This study provides an effective and scalable solution for IIoT‐driven intelligent management of chronic diseases.