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.
