Implementation of various machine learning algorithms to predict the disease from symptoms given by user
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:
Naive Bayes Decision Tree Random Forest 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.