MediForecast

Inspiration

Driven by the need for tools that help healthcare providers identify health risks early, we set out to design a system focused on conditions like Parkinson's and heart disease. The goal is to improve patient outcomes through proactive interventions.

What it does

MediForecast uses predictive analytics to evaluate the likelihood of diseases based on a patient's health indicators. It supports healthcare providers in making timely decisions to prevent health issues from escalating.

How we built it

We collected and cleaned relevant health datasets, then applied statistical modeling to understand the relationship between health features and disease outcomes. The interface was built with Python and Streamlit to create an interactive tool.

Challenges we ran into

We faced challenges with data quality, particularly missing values. Ensuring the model was both accurate and easy to interpret was essential for its application in healthcare.

Accomplishments that we're proud of

We developed a model that can effectively identify individuals at higher risk, contributing to early disease detection and prevention in healthcare.

What we learned

We learned the importance of data preprocessing, feature selection, and balancing model accuracy with interpretability, especially in healthcare, where decisions can have significant impacts.