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 ]