AI-Integrated Predictive Modeling of Disease Outbreaks Using Large-Scale Healthcare and Environmental Data

Abstract

Efficient and timely prediction of disease outbreaks is essential to timely intervention and effective response to public health. The old surveillance systems are usually behind in reporting and data integration and are less efficient in offering early warnings. This paper seeks to create a predictive model to be integrated with AI that is capable of utilizing the large-scale healthcare and environmental data to enhance outbreak detection and forecasting accuracy. The suggested framework will incorporate the heterogeneous data, i.e., clinical case reports, hospital records, meteorological variables, and other environmental indicators, i. e., temperature, humidity, and air quality. Preprocessing of data, feature engineering and normalization are done to make sure the data is of a good quality. Several machine learning and deep learning models are tested and the best model is obtained based on the performance. The stratified sampling and cross-validation methods are used to train and validate the model to overcome the problem of class imbalance and to increase the generalizability of the model. The experimental findings show that the proposed method has high predictive performance; the values of precision, recall, F1-score, and ROC-AUC are high, meaning that the method is reliable in its detection of outbreak events with the lowest false negatives. It is worth noting that the model is more sensitive in detection of early-stage outbreak in comparison to baseline approaches. The results reflect the possibility of performing a powerful disease surveillance by combining AI with multi-source data. This practice can assist the public health authorities to make proactive decisions, optimizing the allocation of resources and alleviating the effects of the outbreaks of infectious diseases.