Enhanced Risk Assessment of Human Health Through Gated Dual-Path RNN and Gravitational Search Algorithm

The need for precise risk assessment of human health in the healthcare industry has resulted in the integration of advanced technology. This study introduces a unique strategy that combines the Gravitational Search Algorithm (GSA) with Gated Dual-Path Recurrent Neural Networks (RNN) to address the present constraints in risk assessment methodologies. Conventional methods are challenged by the complexity of health-related data and the requirement for real-time risk assessment. The goal of this project is to integrate cutting-edge artificial intelligence and optimization techniques to transform the assessment of risks to human health. Predictions produced by current risk assessment algorithms are frequently inadequate due to their inability to identify complex patterns in health data. Moreover, the accuracy and resilience of these models are hampered by the absence of effective optimization tools. For health risk assessment, there is a noticeable lack of integration between advanced neural network topologies and cutting-edge optimization methods. To close this gap and improve risk prediction accuracy, Gated Dual-Path RNN and Gravitational Search Algorithm are combined. The proposed approach uses the Gravitational Search Algorithm to optimize the model parameters and Gated Dual-Path RNN to take advantage of the temporal relationships found in health data. The model capacity to capture both short- and long-term interdependence is improved by the dual-path architecture, and the gravitational search method effectively adjusts the network parameters. Experiments reveal encouraging outcomes, with increased precision and dependability above conventional risk assessment algorithms.