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Disease Diagnosis Based on Symptoms

This project leverages Machine Learning (ML) and Information Retrieval (IR) techniques to detect diseases based on user-inputted symptoms. It provides detailed information about the top predicted diseases, including treatment recommendations.

Table of Contents

Introduction

Early and accurate disease diagnosis is crucial for effective treatment. This project utilizes both ML and IR methods to predict potential diseases from a given set of symptoms, aiming to assist in early detection and provide relevant treatment information.

Features

  • Symptom Input: Users can input a combination of symptoms.
  • Disease Prediction: The system predicts possible diseases based on the input symptoms.
  • Treatment Recommendations: Provides information on treatment options for the predicted diseases.
  • Two Approaches:
    • TF-IDF Cosine Similarity: Uses term frequency-inverse document frequency and cosine similarity for disease prediction.
    • Machine Learning Models: Implements various ML algorithms for prediction.

Dataset

The dataset includes records of diseases and their associated symptoms.

Installation

  1. Clone the Repository:

    git clone https://github.com/abhinavsaurabh/Disease-Diagnosis-based-on-Symptoms.git
cd Disease-Diagnosis-based-on-Symptoms

  2. Set Up a Virtual Environment (optional but recommended):

    python -m venv env
source env/bin/activate  # On Windows: env\Scripts\activate

  3. Install Dependencies:

    pip install -r requirements.txt

Usage

TF-IDF Interaction

  1. Files Required:

    • combination (3).csv
    • cosine_results.py
    • generatesymptoms.py
    • Interaction_TF_IDF_Cosine.ipynb
    • normal (3).csv
    • synonyms.py
    • tf_idf_result.py
    • tokenizer.py

  2. Instructions:

    • Ensure all the above files are in the same directory.
    • Open and run Interaction_TF_IDF_Cosine.ipynb sequentially in a Jupyter Notebook environment.
    • For Google Colab users, upload all the files in the same runtime session and execute the notebook.

Machine Learning Interaction

  1. Files Required:

    • combinational (3).csv
    • decisionT.py
    • IRadaboost.py
    • IRgdb.py
    • IRsvm.py
    • IRxgb.py
    • knneigbh.py
    • LR.py
    • mnb.py
    • multiLP.py
    • normal (3).csv
    • randomrf.py
    • synonyms.py
    • IR_final_ML_interaction_.ipynb

  2. Instructions:

    • Place all the listed files in the same directory.
    • Open and execute IR_final_ML_interaction_.ipynb sequentially in Jupyter Notebook.
    • For Colab users, upload all files in the same runtime session before running the notebook.

Results

The performance of various models is as follows:

Model Accuracy (%)
Multi-Layer Perceptron 89.42
Decision Tree 72.62
Random Forest 89.97
Logistic Regression 89.88
K-Nearest Neighbour 89.97
Support Vector Machine 87.99
Multinomial Naive Bayes 81.30
Gradient Boosting Machine 80.94
Extreme Gradient Boosting 78.83

Contributors

License

This project is licensed under the MIT License. See the LICENSE file for details.