Inspiration
Diabetes is a major public health problem that is approaching epidemic proportions globally. People delay the test, by creating various kinds of excuses. I lost someone very close just because of this neglect. This app is made to give people an idea of how serious their situation is so that they ultimately get a doctor's opinion.
What it does
The web app takes 10 inputs and predicts the chance of having diabetes. It then suggests some remedies for certain input parameters. The input parameters are : age,polyuria,polydipsia,gender,partial_paresis,weight_loss,irritability,healing,alopecia,itching.
How we built it
The model is built using Random Forest Classifier. The web app is built using streamlit.
Challenges we ran into
- Getting a good dataset with enough values for difficulty.
- Trained the model on different algorithms but Random Forest Classifier ultimately gave an accuracy of 98.72%. I also referred to a research paper that is added to the Github repo
Accomplishments that we're proud of
- The model is predicting well even on foreign data
- The web app is smooth
What we learned
- How to pickle a model.
- How to find a correlation between features and the target.
What's next for Diabetes Predictor
Creating a real-time app that keeps track of the health of a person along with many other features.
Built With
- google-colab
- python
- streamlit
Log in or sign up for Devpost to join the conversation.