Disease Prediction and Medicine Recommendation using Machine Learning

This project aims to predict diseases based on symptoms using machine learning models and recommend medicines based on the predicted disease. The project utilizes multiple classifiers and combines their predictions to improve accuracy.

Table of Contents

• Installation
• Usage
• Project Structure
• Models
• Evaluation
• Contributing
• License

Installation

Clone the repository:

git clone https://github.com/Lokesh-DataScience/Disease-prediction.git
cd Disease_prediction

Create a virtual environment and activate it:

python -m venv venv
./venv/Scripts/activate  # On Windows
# On Linux/macOS: source venv/bin/activate

Install the required packages:

pip install -r requirements.txt

Usage

Data Preprocessing:

Load and clean the dataset using the load_and_clean_data function from src/data_preprocessing.py.

Model Training:

Train the models using the train_models function from src/model_training.py.

Model Evaluation:

Evaluate the models using the evaluate_model function from src/model_evaluation.py.

Disease Prediction:

Predict diseases based on symptoms using the predict_disease function from src/disease_prediction.py.

Medicine Recommendation:

Recommend medicines based on the predicted disease using the get_medicines function from views.py.

Run the Main Script:

python main.py

Execute the main script to train models, evaluate them, and make predictions.

Project Structure

Disease_prediction/
├── dataset/
│   ├── Training.csv
│   ├── Testing.csv
├── models/
│   ├── svm_model.pkl
│   ├── nb_model.pkl
│   ├── rf_model.pkl
│   ├── encoder.pkl
│   ├── symptom_index.pkl
├── notebooks/
│   ├── Disease_prediction.ipynb
├── src/
│   ├── data_preprocessing.py
│   ├── model_training.py
│   ├── model_evaluation.py
│   ├── model_inference.py
│   ├── disease_prediction.py
│   ├── utils.py
├── api/
│   ├── disease_prediction/
│   │   ├── templates/
│   │   │   ├── disease_prediction/
│   │   │   │   ├── index.html
│   │   ├── static/
│   │   │   ├── css/
│   │   │   │   ├── styles.css
│   │   │   ├── js/
│   │   │   │   ├── script.js
│   │   ├── views.py
│   │   ├── urls.py
│   │   ├── data/
│   │   │   ├── medicines_data.json
│   ├── healthcare/
│   │   ├── settings.py
│   │   ├── urls.py
│   │   ├── wsgi.py
│   │   ├── asgi.py
│   ├── manage.py
├── medication_recommender/
│   ├── datasets/
│   │   ├── data/
│   │   │   ├── medicines_data.json
│   │   ├── scrape_data/
│   │   │   ├── get_data.py
├── .gitignore
├── README.md
├── requirements.txt
├── main.py

Models

The project uses the following machine learning models:

• Support Vector Machine (SVM)
• Naive Bayes (GaussianNB)
• Random Forest

Evaluation

The models are evaluated using accuracy and confusion matrix. The evaluation results are displayed using seaborn heatmaps.

Contributing

Contributions are welcome! Please fork the repository and submit a pull request.

License

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