❤️ Cardiovascular Disease Risk Predictor

A production-ready machine learning web application for cardiovascular disease risk prediction, built with Streamlit and powered by a GradientBoostingClassifier trained on 70,000 patient records from the Kaggle Cardiovascular Disease dataset. The model uses 12 clinically relevant and lifestyle-linked features, including age, BMI, blood pressure, cholesterol, glucose, smoking, alcohol consumption, and physical activity, and achieves approximately 73% accuracy in binary risk classification.

Live Demo

Try the deployed app here:

• https://cardiovascular-disease-risk-predictor.streamlit.app/

Features

• Real-time BMI calculation from height and weight inputs
• Risk probability gauge/progress indicator for predicted cardiovascular risk
• Blood pressure category display (Normal, Elevated, High)
• BMI category display (Underweight, Normal, Overweight, Obese)
• 12-feature patient input form aligned with model training schema

Dataset

This project uses the Kaggle Cardiovascular Disease Dataset (Sulianova).

• Source: https://www.kaggle.com/datasets/sulianova/cardiovascular-disease-dataset
• Total records: 70,000
• Format: semicolon-separated (;) CSV

Feature Schema

Feature	Description
age	Age in days (converted to years during preprocessing)
gender	Biological sex encoded as categorical value
height	Height in centimeters
weight	Weight in kilograms
ap_hi	Systolic blood pressure
ap_lo	Diastolic blood pressure
cholesterol	Cholesterol category
gluc	Glucose category
smoke	Smoking status
alco	Alcohol consumption status
active	Physical activity status
cardio	Target label (0: low risk, 1: cardiovascular disease risk)

ML Pipeline

The model development workflow follows this sequence:

1. Data cleaning (including outlier removal)
2. Feature engineering (BMI creation and age conversion from days to years)
3. Standardization using StandardScaler
4. Dimensionality reduction using LinearDiscriminantAnalysis (LDA)
5. Classification using GradientBoostingClassifier

How to Run Locally

git clone https://github.com/tejaswirajgit/cardio
cd cardio
pip install -r requirements.txt
python train_save_model.py
streamlit run app.py

Research Reference

This project is aligned with the research work:

Cardiovascular Disease Prediction and Prevention: Exploring Novel Techniques and Applications Using Machine Learning
12th International Conference, 2023
By Tejaswi Raj

Author

Tejaswi Raj
ML Research Engineer
Chandigarh University | DRDO | IIT Mandi
GitHub: https://github.com/tejaswirajgit