Unraveling health impacts of individuals in industrial zones: Leveraging game theory-based LSTM approach for predictive analysis of public health dynamics

Abstract

In recent years, the proliferation of industrial zones has raised concerns about the potential health impacts on nearby populations. Understanding and predicting these effects are crucial for policymakers and public health officials to implement targeted interventions. This study proposes a novel approach that combines game theory and Long Short-Term Memory (LSTM) networks to unravel the complex dynamics of public health in industrial zones. By incorporating game theory into predictive analysis, it can capture the multi-agent nature of decision-making processes that influence public health outcomes in industrial areas. Additionally, LSTM networks, a type of recurrent neural network, is well-suited for modeling the temporal aspects of health dynamics. The proposed framework uses historical data on industrial activities, environmental factors, demographic characteristics, and health outcomes to train the LSTM model. By integrating game theory principles, the model considers the strategic behavior of different factors and their impact on public health indicators over time. Through iterative learning and optimization, the model can generate predictive insights into future health trajectories in industrial zones. This application-driven framework leverages existing synergies between game theory and deep learning to model the strategic and temporal dynamics of public health in industrial zones. Overall, the integration of game theory and LSTM-based predictive analysis offers a promising avenue for understanding and addressing the health impacts of industrialization. The proposed method is implemented in Python software and has an accuracy of about 99.12 % which is 3.1 % higher than other existing methods like HealthFog, Conv LSTM and GoogleNet- Deep Neural Network (DNN).