Predicting disease incidence using social security data: Using deep learning models to analyze and predict disease prevalence based on insurance data: A Comprehensive Survey

This review article discusses the growing role of deep learning models in predicting disease occurrences using social security data. With advancements in machine learning, the integration of vast databases from social security systems offers a new avenue for public health research, enhancing disease prediction accuracy and resource allocation. The introduction emphasizes the criticality of accurate disease forecasting in healthcare planning and highlights the need for technological integration in epidemiological studies. The literature review explores various studies that have employed different machine learning algorithms for disease outbreak predictions. It assesses the strengths and limitations of traditional statistical methods versus modern deep learning frameworks, aiming to identify best practices and gaps in existing research. In the methodology section, a systematic approach is established for analyzing the efficacy of deep learning models in disease prediction. This includes criteria for dataset selection and an overview of leveraging social security data. Specific deep learning architectures, particularly convolutional neural networks (CNNs) and recurrent neural networks (RNNs), are discussed for their effectiveness in processing complex datasets. The analysis section presents a detailed examination of model performance metrics from predictive experiments, showcasing significant improvements in accuracy and prediction timelines compared to conventional models. Visualization techniques aid in elucidating the results, demonstrating the transformative potential of predictive analytics in public health surveillance. In the discussion and conclusion, the article reflects on the implications of findings for real-world healthcare applications and emphasizes ethical considerations around data privacy and model transparency. It advocates for the integration of predictive models into public health frameworks and proposes future research directions, including hybrid models and real-time data usage, highlighting the promising role of deep learning in enhancing disease forecasting.

کلیدواژه ها:

Artificial Intelligence ، Deep Learning ، social security data ، disease prediction ، Machine Learning