CardioSense

Early Detection, Smarter Protection

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CardioSense – Early Detection, Smarter Protection

Problem Statement:

Cardiovascular diseases (CVDs) are the leading cause of death globally, claiming nearly 17.9 million lives annually (according to WHO). What’s alarming is that over 80% of these deaths are preventable with early detection and lifestyle changes. Unfortunately, millions remain undiagnosed due to limited awareness and lack of accessible, affordable risk assessment tools—leading to late-stage intervention and higher mortality.

Inspiration:

I was motivated by the shocking preventability of most heart disease-related deaths and wanted to design a tool that empowers everyday people with awareness through accessible technology.

What it does:

CardioSense is a web-based Machine Learning application that predicts an individual’s cardiovascular disease risk using simple health inputs such as age, blood pressure, cholesterol, and lifestyle factors. It delivers clear, actionable results through a minimal, intuitive interface, ensuring accessibility even for non-technical users.

How I built it:

  • Dataset: Cardiovascular Diseases Risk Prediction Dataset (60,000+ patient records).link
  • Model: Implemented in Python using scikit-learn, with Logistic Regression chosen for its interpretability and reliable performance.
  • Web App: Built with Flask (backend) and HTML, CSS, JS (frontend) for deployment.

Features:

  • Simplistic UI for ease of use
  • Mobile-friendly design for accessibility
  • Fast, real-time predictions
  • Gradient-styled header and clean form inputs for a professional yet approachable look

Challenges I ran into:

  • Balancing accuracy and recall to ensure high-risk individuals aren’t missed
  • Designing a uniform UI across inputs for consistency
  • Deploying the ML model seamlessly into a user-facing web app

Accomplishments that I'M proud of:

  • Built a fully working prototype that combines ML and web development
  • Achieved a user-friendly experience while maintaining technical robustness
  • Created a tool that demonstrates the practical role of ML in preventive healthcare

What I learned:

  • Handling real-world healthcare datasets (preprocessing, feature engineering, balancing metrics)
  • Importance of UI/UX design in increasing accessibility of technical tools
  • Building and deploying an ML model into a web app workflow

What's next for CardioSense:

I aim to:

  • Integrate wearable device data for continuous monitoring
  • Explore deep learning to improve prediction accuracy
  • Add interactive dashboards for long-term health tracking
  • Partner with healthcare organisations to bring preventive screening closer to communities
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