3Goutam / Disease-Prediction-from-Symptoms

Disease Prediction based on Symptoms.

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 ]