CardioSense

CardioSense is a multimodal AI system that combines clinical data, ECG signals, and explainable machine learning to detect cardiovascular disease early and provide interpretable risk predictions.

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Inspiration

Cardiovascular disease remains the leading cause of death worldwide. Many heart conditions are detected only after symptoms become severe, which reduces the chances of effective treatment. Early detection and risk prediction can significantly improve patient outcomes.

This project was inspired by the idea that modern machine learning can help doctors identify cardiovascular risk earlier by analyzing patterns in clinical data and ECG signals. By combining different sources of medical data, we aimed to build a system that not only predicts heart disease risk but also explains why the prediction was made.

Our goal was to create a practical AI system that supports early diagnosis while remaining transparent and interpretable.

What the Project Does

CardioSense is a multimodal AI system designed to detect cardiovascular risk using multiple types of biomedical data.

The system performs three main tasks:

1. Early Detection of Heart Disease
Using clinical patient features such as blood pressure, cholesterol levels, heart rate, and ECG indicators, an ensemble machine learning model predicts the probability of cardiovascular disease.

2. Cardiovascular Risk Forecasting
A second model analyzes large-scale cardiovascular health data to estimate long-term risk patterns across a population.

3. ECG Signal Analysis with Deep Learning
A convolutional neural network analyzes ECG time-series signals to detect abnormal cardiac patterns.

These outputs are combined into a Unified Cardiovascular Risk Score, giving a more complete view of patient health.

To make predictions transparent, we integrated Explainable AI using SHAP, which highlights the most important features influencing each prediction.

The system also generates a simple risk report that includes:

  • Estimated cardiovascular risk percentage
  • Risk category (Low, Moderate, High, Critical)
  • Key contributing health factors

How We Built It

The project combines several machine learning and deep learning techniques.

Data Processing

Multiple cardiovascular datasets were cleaned and preprocessed. Features were normalized and class imbalance was handled using SMOTE oversampling.

Early Detection Model

We implemented a stacking ensemble model combining:

  • Random Forest
  • XGBoost
  • LightGBM

This approach improves predictive performance by combining multiple algorithms.

Risk Forecast Model

A separate XGBoost model was trained on a larger cardiovascular dataset to estimate long-term cardiovascular risk.

ECG Deep Learning Model

ECG signals were processed using a 1D Convolutional Neural Network (CNN) to learn patterns directly from waveform data.

Explainable AI

To ensure transparency, we used SHAP (Shapley Additive Explanations) to identify which clinical features contributed most to each prediction.

Multimodal Risk Integration

Finally, predictions from the clinical model, risk forecasting model, and ECG model were combined to compute a Unified Cardiovascular Risk Score.

Challenges We Faced

One of the biggest challenges was handling large biomedical datasets, particularly ECG time-series signals. These signals contain thousands of data points per sample and require careful preprocessing to train efficiently.

Another challenge was class imbalance, which is common in medical datasets. We addressed this using SMOTE to balance the training data.

We also focused on improving model interpretability, since medical AI systems should not behave like black boxes. Integrating SHAP allowed us to explain predictions in a meaningful way.

What We Learned

Through this project we gained practical experience in:

  • Ensemble machine learning techniques
  • Deep learning for biomedical signal analysis
  • Handling imbalanced healthcare datasets
  • Implementing explainable AI
  • Designing multimodal AI systems

Most importantly, we learned how AI can support healthcare decisions by combining accuracy, interpretability, and practical usability.

Future Improvements

Future versions of CardioSense could include:

  • Integration with wearable device data
  • Real-time ECG monitoring
  • Larger clinical datasets for improved accuracy
  • A web or mobile interface for patient access
  • Clinical validation with healthcare professionals

Our long-term vision is to develop CardioSense into a decision-support tool that helps clinicians detect cardiovascular risk earlier and more effectively.

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