samhan-6/kidneystoneprediction

KidneyStonePrediction

This project uses a machine learning model to predict whether a patient is likely to develop kidney stones. The model is trained on a dataset of patients who have developed kidney stones and patients who have not developed kidney stones. The model uses the following features to make predictions:

• Gravity of urine
• pH of urine
• Osmolality of urine
• Conductivity of urine
• Urea concentration in urine
• Calcium concentration in urine

KidneyStonePrediction Installation

1. First You need to install anaconda.
2. open anaconda and create new envirement
3. click the new envirement and click on play button and then select 'open terminal'
4. in the terminal, you need to install streamlit if you don't have one. To instal type 'pip install streamlit'
5. once you install streamlit, just write 'streamlit run kidney_stone_prediction_web.py'
6. app will be open in your default browser
7. once open you need to fill some values inorder to predict if the patient have kidney stone or not.
8. The prediction output will be 0 or 1
9. 0 indicates the patient doesn't have kidney stone and 1 indicates the patient have a kidney stone

Features

The project includes the following features:

• The ability to predict whether a patient is likely to develop kidney stones
• A user-friendly interface
• A detailed README.md file

Limitations

The project has the following limitations:

• The model is not perfect and may make incorrect predictions
• The model is only trained on a limited dataset of patients