healthcare prediction system with WEATHER FORECASTING

Abstract

Our project introduces a new type of healthcare pre front-end interface with a powerful MongoDB transform health prediction. Its main objective is to Random Forest algorithm to of medical predictions by fusing weather information increase the precision individual's medical history and symptoms. The project aims health by combining historical medical data, relationship between climate and with an to uncover the demographics, and clinical outcomes, with including disease outbreaks, patient seasonal climate changes such as temperature, humidity, air quality, and precipitation. complex prediction models using random forest algorithms and The system creates advanced statistical techniques. This model represents a significant advance in medical particular disease or health a planning strategies by predicting the likelihood of weather conditions. Seamlessly integrating weather facts into condition based on user with instant forecasts and tailored medical systems will provide the recommendations. This approach ensures that people are immediately warned of tial health risks associated with certain weather conditions, allowing them to seek poten preventive strategies. Finally, the program aims to improve healthcare planning and allocation by integrating forest-based climate data into health forecasting and resource management, promising better prevention, quality control and improved public health outcomes. The React front provides a responsive, user-friendly experience, while the powerful and reliable framework that supports MongoDB backend provides a evidence-based decision-making in healthcare, empowering the vulnerable, doctors, and policymakers.