AI-Based Disease Prediction System

The AI-Based Disease Prediction System is an advanced web application developed with Flask and Python that leverages machine learning to predict diseases based on user-selected symptoms. Using a Random Forest classifier trained on a comprehensive health dataset, the application allows users to select multiple symptoms and quickly receive accurate, real-time predictions. It also provides detailed disease descriptions, precautionary measures, and enables easy retraining of the model with updated data, ensuring that the prediction system stays current with evolving medical knowledge.

Features & Functionality

This system offers an intuitive multi-symptom input interface where users can specify their symptoms. The backend processes these inputs through the Random Forest machine learning model, which has been trained to classify diseases effectively. As a result, users receive instant disease predictions along with valuable details such as symptom descriptions, precaution suggestions, and further guidance. The web app features a clean and responsive frontend design ensuring seamless access across various devices.

Future Scope and Enhancements

• Integration with Medical APIs: Incorporate real-time data from health organizations and medical databases to improve prediction accuracy and update datasets.
• Advanced ML Models: Experiment with deep learning models or ensemble learning techniques for enhanced prediction and early diagnosis.
• Personalized Recommendations: Augment the system with personalized medication, diet, and lifestyle recommendations based on patient history.
• Mobile Application: Develop companion mobile apps for Android and iOS to increase accessibility.
• Multi-language Support: Enable multilingual support to reach a broader demographic.
• Telemedicine Integration: Integrate a consultation feature allowing users to connect with healthcare professionals directly.
• Data Privacy & Security: Implement robust encryption, compliance with healthcare regulations like HIPAA, and anonymized data handling.
• Expanded Dataset: Continuously collect and incorporate new medical data to improve model robustness and accuracy.
• User Feedback Loop: Allow users to provide feedback on predictions, enhancing supervised learning and fine-tuning the model.

Installation Guide

Prerequisites:

• Python 3.7 or higher
• pip package manager
• Git

Steps:

1. Clone the repository:

bash
git clone https://github.com/navingohite/disease-prediction-ml.git
cd disease-prediction-ml

1. Create and activate a virtual environment:

bash
python -m venv venv
source venv/bin/activate  # For Windows: venv\Scripts\activate

1. Install required dependencies:

bash
pip install -r requirements.txt

1. Prepare the dataset and place CSV files (dataset.csv, symptom_Description.csv, symptom_precaution.csv) in the appropriate directory.
2. Run the Flask app:

bash
python app.py

1. Access the application via a web browser at:

text
http://localhost:5000

Summary

This AI-based disease prediction system offers a powerful tool for disease diagnosis leveraging machine learning and user-friendly web technologies. Its extensible architecture supports continuous improvements, making it a valuable healthcare solution that empowers users and clinicians alike with timely and accurate medical predictions.