Epidemic Forecasting Using an Improved frequent Pattern Procedure and Multivariable Time-Based Association Pattern Extraction

Abstract: Global health crises pose risks to the global healthcare system and economic systems. Preliminary forecasting of an illness epidemic may help administrations and healthcare institutions perform precautionary measures. The article suggests an improved frequent pattern growth method. It gets put together with extracting patterns from time-based relationships across multiple dimensions to help with health crisis forecasting. Conventional frameworks rely primarily on automated training and quantitative methods. This approach may overlook some latent time-based associations in the medical data. It seems like those connections could matter, but they are often missed. Maybe the time aspects are what stand out here. There is more to consider with how the data actually flows over periods. This suggested picture combines medical data, Weather-related variables, Population-based data, time-based variables, and the calendar month to detect epidemic structures. That identified relationship pattern may assist a timely alert framework and enhance the formulation of community healthcare policy.

Keywords: Global health crisis, Association rule, Frequent pattern growth algorithm, medical care data analysis, Data extraction.