🫀 Heart Risk Prediction Web App

This is a simple yet powerful heart disease risk prediction system built using Python, Scikit-learn, and Flask. The app predicts a numerical risk score for heart disease based on patient lifestyle and medical history.

🔍 Project Overview

The main objective of this project is to provide a basic regression-based heart risk prediction using machine learning. The model is trained using real-world health-related features, allowing users to estimate their risk level based on individual inputs.

📊 Features Used

• Sex (male/female)
• Age
• Total Cholesterol (TC)
• HDL Cholesterol (HDL)
• Smoking status (smoking / no smoking)
• Blood Pressure Medication (yes / no)
• Diabetes (yes / no)

🧪 ML Pipeline

• Preprocessing:
  • Handling missing values
  • Feature scaling
  • Categorical encoding
• Model: GradientBoostingRegressor
• Validation: Cross-validation & Hyperparameter tuning using GridSearchCV

🖥️ Flask Web App

A simple UI that allows users to:

• Input personal and medical data
• Get a real-time heart disease risk score
• Visualize prediction instantly

Currently hosted locally via Flask.

🚀 Getting Started

1. Clone the repository:

   git clone https://github.com/dmisasanka2002/Heart-Risk-Prediction.git
   cd Heart-Risk-Prediction

2. Install dependencies:

   pip install -r requirements.txt

3. Run the app locally:

   python app/main.py

📁 Folder Structure

📦Heart Risk Prediction
 
 ┣ 📂app/ # Flask application
 ┣ 📂data/ # Data and data models
 ┣ 📂models/ # ML pipeline and saved model
 ┣ 📂notebooks/
 ┣ 📜README.md
 ┗ 📜requirements.txt

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