DocAssist

A Machine Learning project that helps doctors detect whether patients need treatment or not based on the Blood Analysis Dataset.

Project Setup and Execution

Step 1: Clone the Repository or Download the ZIP

Clone the repository or download the ZIP file from the following Git link: https://github.com/Vishal4198/DocAssist

Step 2: Folder Structure

Ensure your project has the following folder structure:

Step 3: Open and Run the Notebook

1. Open the sourcecode.ipynb file from the notebooks folder.
2. Run each cell in the Jupyter Notebook or VScode.

Step 4: Trained Model Creation

After executing sourcecode.ipynb, the model.joblib file will be created inside the models folder, which is the trained model for this project.

Step 5: Running the Flask App

Using VS Code

1. Open the project in VS Code.
2. Locate app.py, which contains the Flask code for designing the user interface and uses HTML for webpage design.
3. Before running the app.y ensure model.joblib is present in the models directory and is updated.
4. Run app.py in VS Code.
5. The terminal at the bottom of the VS Code screen will show a local connection for the webpage.
6. Hold Ctrl + click to open the webpage in your default browser or you can copy and paste it your desired browser.
7. You can now see the user interface for entering the requested data. After inputting the data, click "Submit" to predict the output using the pre-trained model model.joblib.
8. Once you finish. Ctrl + C on the terminal to stop the application app.py.

Using Command Prompt or PowerShell

1. Open the Command Prompt or PowerShell from the project folder.
2. Before running the app.y ensure model.joblib is present in the models directory and is updated.
3. Run the following command:

   python app.py

4. After running the command, a local connection link for the webpage will be displayed. Click on the link to open the webpage in your default browser or copy and paste it your desired browser. Input your data, and the output will be predicted using the pre-trained model.
5. Once done Ctrl + C on the terminal to stop the application app.py.

Sample data for testing

Male sample data

HAEMATOCRIT	HAEMOGLOBINS	ERYTHROCYTE	LEUCOCYTE	THROMBOCYTE	MCH	MCHC	MCV	AGE	SEX
48.5	16.5	5.63	8	299	29.3	34	86.1	29	M
31.7	10.9	3.84	15.1	45	27.3	33.1	82.6	71	M

Female sample data

HAEMATOCRIT	HAEMOGLOBINS	ERYTHROCYTE	LEUCOCYTE	THROMBOCYTE	MCH	MCHC	MCV	AGE	SEX
35.4	11.9	3.9	5.3	243	30.5	33.6	90.8	35	F
31.7	10.6	3.74	8.5	268	28.3	33.4	84.8	24	F