This Diabetes Predictor leverages Random Forest Classification Model to predict diabetes risk based on individual medical history and demographic information. By analyzing factors such as age, gender, body mass index (BMI), hypertension, heart disease, smoking history, HbA1c level, and blood glucose level, this application assists healthcare professionals in identifying patients at risk of developing diabetes.
It uses Streamlit to create a web application that can be used to make predictions by entering the patient details and obtaining results.
To use the streamlit web application to make predictions, follow these steps:
- Clone the repository: git clone https://github.com/paramveerkaur1/diabetes-prediction-using-rfc-streamlit.git
- Install the required packages: pip install -r requirements.txt
- Run the Streamlit app: streamlit run app.py
- Access the app in your browser at http://localhost:8501
Streamlit Output:
