CardioPredict-Ai

CardioPredict-Ai is a machine learning-powered web application that provides a simple and interactive user interface to predict a person's risk of heart disease based on various health metrics and medical attributes.

The app uses a trained Logistic Regression model along with a pre-fitted scaler to process user inputs and deliver an instant prediction.

Features

• Interactive UI: Sliders, dropdowns, and number inputs to easily capture patient data.
• Real-time Prediction: Click "Predict" to instantly evaluate the risk of heart disease.
• Comprehensive Medical Inputs:
  • Age & Sex
  • Chest Pain Type
  • Resting Blood Pressure & Cholesterol
  • Fasting Blood Sugar
  • Resting ECG Results
  • Maximum Heart Rate
  • Exercise-Induced Angina
  • ST Depression (Oldpeak) and ST Slope

Screenshots

Installation and Setup

1. Clone the repository:

   git clone <your-repo-url>
   cd "CardioPredict Ai"

2. Install the required dependencies: Ensure you have Python 3 installed. Then, run:

   pip install -r requirements.txt

3. Run the application:

   streamlit run app.py

4. Access the Web App: Open your browser and navigate to http://localhost:8501.

Project Structure

• app.py: The main Streamlit application script.
• HeartDiseasePredictor.ipynb: Jupyter Notebook containing the data exploration, preprocessing, and model training steps.
• Logistic_Regression_Heart_Model.pkl: The trained Logistic Regression model.
• scaler.pkl: The saved scaler used to normalize input data before prediction.
• columns.pkl: The expected feature columns for the model.
• heart.csv: The dataset used to train the machine learning model.
• requirements.txt: Python dependencies required to run the project.

Dependencies

• streamlit
• pandas
• numpy
• scikit-learn
• joblib