https://github.com/malharinamdar/diabetescare-ai

diabetes prediction system
https://github.com/malharinamdar/diabetescare-ai

gemini-flash hyperparameter-tuning random-forest streamlit-webapp

Last synced: 6 months ago
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diabetes prediction system

• Host: GitHub
• URL: https://github.com/malharinamdar/diabetescare-ai
• Owner: malharinamdar
• Created: 2024-07-12T16:07:57.000Z (about 1 year ago)
• Default Branch: main
• Last Pushed: 2024-12-11T19:02:30.000Z (10 months ago)
• Last Synced: 2025-02-10T15:24:18.698Z (8 months ago)
• Topics: gemini-flash, hyperparameter-tuning, random-forest, streamlit-webapp
• Language: Jupyter Notebook
• Homepage: https://diabetescare-ai-tech.streamlit.app/
• Size: 7.27 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# DiabetesCare-AI 

personalised diabetes prediction along with a Q & A Chatbot

   

## File Structure

- `finalapp.py`: The main script for running the Streamlit web app.

- `requirements.txt`: Lists all the dependencies required for the project.

- `wowmodel2.pkl`: The pre-trained machine learning model.

- `diabetes_prediction_dataset.csv`: The dataset used for predictions and visualizations.

   

### Preprocessing, Model Building and Hyperparameter Tuning

- Conducted one-hot encoding categorical features

- Implemented `Random Forest` with `GridSearchCV` for Hyperparameter Tuning

  

### Results:

- Selected best parameters after cross-validation.

- Evaluated model performance on the test set.

- Achieved high accuracy `0.94` on the test set.

  

## Web App Components

1. **User Authentication**

   

    Users can log in using their name.

    The session state is used to manage user accounts and their prediction history

2. **Data Input**

   

    The sidebar allows users to input various health indicators such as `gender`, `age`,

    `hypertension`, `heart disease`, `smoking history`, `height`, `weight`, `HbA1c level`, and `blood glucose level`.

   

    BMI is calculated automatically based on the height and weight inputs.

3.  **Prediction**

   

    The `predict_button` triggers the prediction function which uses the pre-trained model to predict the likelihood of diabetes.

    The prediction result is displayed to the user.

    

    The pre-trained achieving an accuracy of `94%` on a `100000` large dataset was loaded as a `.pkl` file

    into the `streamlit` code.

4. **Visualisation**

   

   `Seaborn` and `Matplotlib` enables the users to examine the relationship between the features.

   Multiple graphs integrated to provide a comprehensive overview of the `input data` and the `diabetes_prediction_dataset.csv`.

   If diabetic the plot on the graph is a unique circle with a shade of `red`, else the circle plotted is dark shade of `blue`.

5. **Suggestions**

    

   Personalised lifestyle and dietary suggestions, including helpful resources of hospitals in India are provided

   by the integrtaion of `gemini-1.5-flash` LLM model.

   Sutable `safety_settings` and `temperature` was configured along with the **nucleus sampling** of the

   `top_k` and `top_p` temperature

   Kept maximum output of `4096` tokens at a time for the assistance.

6. **Q & A Chatbot**

    

   The website hosts a Q & A Chatbot to answer queries arising by patients. The history of queries entered by the user are saved and

   displayed in the end.

   The chatbot leverages the use of `gemini-1.5-flash` LLM Model.