Abstract:
Prediction of disease events and flares is of paramount importance in modern healthcare. Traditional models relied heavily on historical data but did not always provide a comprehensive view, often lacking essential spatial information necessary to comprehend disease dynamics. GeoHealth fills these gaps by seamlessly combining GIS with machine learning. The combination of GIS and machine learning enables an in-depth analysis of the disease dispersion pattern using historical and contemporary data, providing both detailed and granular insights. One of the key advantages of GeoHealth is its robustness, with an accuracy rate of 86 percent. GeoHealth provides users with the ability to upload data sets, validate data sets, choose specific analysis modes, delineate geographical regions, and select data timelines for comprehensive disease visualization. GeoHealth’s architecture is designed to be highly resilient and scalable, allowing it to effectively manage large datasets. This allows for rapid processing and precise disease prediction, which is essential for timely treatment. GeoHealth is revolutionary. By combining Geographic Information Systems (GIS) with machine learning and providing unrivaled precision, it has the potential to revolutionize the way health organizations manage and address disease trends.
