Disease Prediction and Medical Recommendation System 🩺

This repository contains the implementation of a Disease Prediction and Medical Recommendation System developed for the CS 584 Machine Learning course.

Introduction

The Disease Prediction and Medical Recommendation System leverages machine learning techniques to predict diseases based on user-entered symptoms. It provides recommendations for medications, diets, and workouts tailored to specific diseases. The project uses a dataset from Kaggle comprising symptoms, diseases, medications, and other medical attributes to train and evaluate machine learning models.

Project Overview

The project files are organized into the following directories:

1. kaggle_dataset

• description.csv: Descriptions of diseases.
• diets.csv: Recommended diets for diseases.
• medications.csv: Medications prescribed for diseases.
• precautions_df.csv: Precautions to be taken for diseases.
• Symptom-severity.csv: Severity of symptoms.
• symptoms_df.csv: Symptoms with corresponding disease labels.
• Training.csv: Dataset for training machine learning models.
• workout_df.csv: Recommended workouts for diseases.

2. model

• RandomForest.pkl: Trained Random Forest model for disease prediction.

3. templates

• index.html: Frontend interface for the Disease Prediction System.

4. static

• bgCover.jpg, img.png : Images utilized in the frontend webpage.

5. screenshots

• Includes screenshots of the project.

How to Run the Project

To run the Disease Prediction and Medical Recommendation System:

1. Install required Python libraries:

   pip install pandas scikit-learn flask ast numpy fuzzywuzzy pickle
   

2. Navigate to the project directory:

   cd ML_project
   

3. Start the Flask application:

   python main.py
   

4. Access the web interface in your browser at:

   http://localhost:5000
   

Team Members

• Tanmay Pramanick - A20541164
• Kunal Rajput - A20540912
• Soham Sonar - A20541266

Project Files Overview

• main.py: Entry point for the Flask web application.
• disease_prediction_system.ipynb: Jupyter Notebook with data preprocessing and model training.
• CS584_Project_Report.pdf: Detailed report on methodologies, model evaluation, results, and future enhancements.
• index.html: Front-end interface for the web application.