AI-DOC

AI-DOC is a medical application. Developed to assist the doctors and the individual patients to comprehend the laboratory data to infer whether the person is suffering from a particular disease or not. Many diseases are analysed in this medical application more diseases would be added later. The application uses multiple machine learning and deep learning models, developed using Flask and deployed to Heroku. It's live at https://ai-doc.herokuapp.com/.

Structure

The directory contains web sub directories and a sub directory for hosting model and other scripts:

1. app.pyThe file which contains all the main backend operations of the website and used to run the flask server locally.

2. Procfile for setting up heroku.

3. requirement.txt contains all the dependencies.

4. templates contains the html file.

5. static contains the css file.

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Codebase

The entire code has been developed using Python programming language and is hosted on Heroku. The analysis and model is developed using SkcitLearn library and various machine learning models, The website is developed using Flask.

How to run the project:

1. Open the Terminal.
2. Clone the repository by entering https://github.com/abhishek-parashar/AI-DOC.
3. Ensure that Python3 and pip are installed on the system.
4. Create a virtualenv by executing the following command: virtualenv venv.
5. Activate the venv virtual environment by executing the follwing command: source venv/bin/activate.
6. Enter the cloned repository directory and execute pip install -r requirements.txt.
7. Now, execute the following command: flask run and it will point to the localhost server with the port 5000.
8. Enter the IP Address: http://localhost:5000 on a web browser and use the application.

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Dependencies

The following dependencies can be found in requirements.txt:

1. Tensorflow
2. scikit-learn
3. Flask
4. Gensim
5. pandas
6. numpy
7. scikit-learn
8. gunicorn

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Down below are the names of the various model files used:

1. Cancer model = model
2. Diabetes model = model1
3. Heart model = model2
4. Liver model = model4
5. Kidney model = model3
6. Malaria model = model111.h5
7. Pneumonia model = my_model.h5

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Details of various datasets used for model development :

1. Cancer : cancer.csv [In the repository]
2. Diabetes : diabetes.csv [In the repository]
3. Heart : heart.csv [In the repository]
4. Liver : https://www.kaggle.com/uciml/indian-liver-patient-records
5. Kidney : https://www.kaggle.com/mansoordaku/ckdisease
6. Malaria : https://www.kaggle.com/iarunava/cell-images-for-detecting-malaria
7. Pneumonia: https://www.kaggle.com/paultimothymooney/chest-xray-pneumonia

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References

2. For Building machine learning model:

1. https://medium.com/themlblog/splitting-csv-into-train-and-test-data-1407a063dd74
2. https://towardsdatascience.com/multi-class-text-classification-model-comparison-and-selection-5eb066197568
3. https://medium.com/@robert.salgado/multiclass-text-classification-from-start-to-finish-f616a8642538
4. https://www.analyticsvidhya.com/blog/2018/04/a-comprehensive-guide-to-understand-and-implement-text-classification-in-python/
5. https://www.districtdatalabs.com/text-analytics-with-yellowbrick
6. Applied AI course- https://www.appliedaicourse.com/

3.For Building the Website and Deploying it:

1. https://towardsdatascience.com/designing-a-machine-learning-model-and-deploying-it-using-flask-on-heroku-9558ce6bde7b
2. https://towardsdatascience.com/deploying-a-deep-learning-model-on-heroku-using-flask-and-python-769431335f66
3. https://medium.com/analytics-vidhya/deploy-machinelearning-model-with-flask-and-heroku-2721823bb653
4. https://www.youtube.com/watch?v=UbCWoMf80PY
5. https://www.youtube.com/watch?v=mrExsjcvF4o
6. https://blog.cambridgespark.com/deploying-a-machine-learning-model-to-the-web-725688b851c7