Multiple Disease Prediction System with Streamlit 🧬

Zain Manna

Data Scientist

Project Description:

The project involves developing a web application for multiple disease predictions using machine learning models. It encompasses building a user-friendly interface that allows prediction for diabetes, heart disease, and Parkinson's disease based on input features tailored to each disease type.

Project Steps:

1. Model Training and Saving:

- Machine learning models were trained for diabetes, heart disease, and Parkinson's disease prediction using respective datasets.

- ML models: Support Vector Machine - Logistic Regression.

2. Web App Development:

- Utilized Streamlit to create a user interface with sidebar navigation for disease selection.

- Implemented specific input fields for each disease type to collect required features for prediction.

- Integrated functionality to load saved models using `pickle` for disease prediction based on user input.

3. UI Development and Deployment:

- Developed the user interface with Streamlit's sidebar for disease selection.

- Implemented conditional rendering to display disease-specific input fields.

- Integrated prediction logic to provide instant disease prediction results based on user input.

Tools Used:

- Python (Streamlit, pickle)

- Machine learning libraries (scikit-learn, pandas)

- Streamlit options menu for creating sidebar navigation

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