https://github.com/anujdutt9/disease-prediction-from-symptoms

Disease Prediction based on Symptoms.
https://github.com/anujdutt9/disease-prediction-from-symptoms

disease-prediction machine-learning pandas python3 scikit-learn scikitlearn-machine-learning

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Disease Prediction based on Symptoms.

• Host: GitHub
• URL: https://github.com/anujdutt9/disease-prediction-from-symptoms
• Owner: anujdutt9
• Created: 2018-04-12T01:09:32.000Z (over 6 years ago)
• Default Branch: master
• Last Pushed: 2023-02-15T01:31:33.000Z (over 1 year ago)
• Last Synced: 2024-04-21T10:33:39.119Z (7 months ago)
• Topics: disease-prediction, machine-learning, pandas, python3, scikit-learn, scikitlearn-machine-learning
• Language: Jupyter Notebook
• Homepage:
• Size: 5.87 MB
• Stars: 256
• Watchers: 15
• Forks: 151
• Open Issues: 1
• Metadata Files:
  • Readme: README.md

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README

        
# Disease Prediction from Symptoms

This project explores the use of machine learning algorithms to predict diseases from symptoms. 

### Algorithms Explored

The following algorithms have been explored in code:

1. Naive Bayes

2. Decision Tree

3. Random Forest

4. Gradient Boosting

# Dataset

### Source-1

The dataset for this problem used with the `main.py` script is downloaded from here:

```

https://www.kaggle.com/kaushil268/disease-prediction-using-machine-learning

```

This dataset has 133 total columns, 132 of them being symptoms experienced by patiend and last column in prognosis for the same.

### Source-2

The dataset for this problem used with the Jupyter notebook is downloaded from here: 

```

https://impact.dbmi.columbia.edu/~friedma/Projects/DiseaseSymptomKB/index.html

```

This dataset has 3 columns:

```

Disease  | Count of Disease Occurrence | Symptom

```

You can either copy paste the whole table from here to an excel sheet or scrape it out using Beautifulsoup.

# Directory Structure

```

|_ dataset/

         |_ training_data.csv

         |_ test_data.csv

|_ saved_model/

         |_ [ pre-trained models ]

|_ main.py [ code for laoding kaggle dataset, training & saving the model]

|_ notebook/

         |_ dataset/

                  |_ raw_data.xlsx [Columbia dataset for notebook]

         |_ Disease-Prediction-from-Symptoms-checkpoint.ipynb [ IPython Notebook for loading Columbia dataset, training model and Inference ]

```

# Usage

Please make sure to install all dependencies before running the demo, using the following:

```

pip install -r requirements.txt

```

## Interactive Demo

For running an interactive demo or sharing it with others, please run `demo.py` using Jupyter Notebook or Jupyter Lab.

```

jupyter notebook demo.ipynb

```

## Standalone Demo

For running the inference on test set or on custom inputs, you can also use the `infr.py` file as follows:

```

python infer.py

```

**NOTE:** ***This project is for demo purposes only. For any symptoms/disease, please refer to a Doctor.***