The Multi-Disease Prediction System is a machine learning-based framework designed to predict the likelihood of diabetes, heart disease, and kidney disease. It leverages advanced preprocessing techniques, dynamic feature engineering, and optimized machine learning models to deliver high accuracy and scalability. The system is deployed as a real-time, user-friendly application using the Streamlit framework.
Disease Prediction: Supports predictions for diabetes, heart disease, and kidney disease.
Advanced Models: Tailored machine learning models for each disease to maximize accuracy.
Real-Time Application: Deployed via Streamlit for instant predictions.
Scalable Design: Modular architecture allows integration of additional diseases.
Comparative Study: Includes benchmarking results for multiple machine learning algorithms.
Programming Language: Python
Machine Learning Libraries: Scikit-learn, Pandas, NumPy
Visualization Tools: Matplotlib, Seaborn
Deployment Framework: Streamlit
Data Collection: Publicly available datasets for each disease.
Preprocessing: Missing value handling, feature scaling, and encoding.
Model Training: Optimized machine learning models tailored for each disease.
Deployment: Web-based application for real-time predictions.
