VitaBeat

Inspiration

Cardiovascular disease remains the leading cause of death worldwide, yet many cases go undetected until advanced stages—especially in low-resource or underserved settings. While hospitals collect large amounts of clinical and ECG data, transforming this data into early, actionable insight remains a challenge. We were inspired to build VitaBeat to bridge this gap: an AI system that not only predicts cardiovascular risk but also explains the reasons behind that risk, empowering clinicians and patients to act earlier and more confidently.

What it does

VitaBeat is a multimodal AI-powered cardiovascular screening system that combines:

1. Clinical tabular data (age, blood pressure, cholesterol, lifestyle factors).
2. ECG time-series signals (heart rhythm patterns).

Using machine learning and deep learning, VitaBeat:

1. Predicts cardiovascular disease risk.
2. Generates a clinically meaningful risk score (low, moderate, high).
3. Detects abnormal ECG patterns.
4. Provides explainable insights into which factors drive each prediction.

The system is designed for early screening, prioritization of high-risk patients, and deployment in scalable healthcare environments.

How we built it

We built VitaBeat as an end-to-end, reproducible machine learning pipeline:

1. Generated medically realistic synthetic datasets using Python to simulate real cardiovascular data.
2. Performed extensive exploratory data analysis and feature engineering.
3. Trained and evaluated multiple tabular ML models, including Logistic Regression, Random Forest, and Gradient Boosting.
4. Built a deep learning model (CNN/LSTM) to classify ECG time-series signals.
5. Designed a risk scoring system from model probabilities instead of relying on binary predictions.
6. Applied model explainability techniques (SHAP) to interpret predictions.
7. Evaluated calibration, error patterns, and reliability to ensure medical robustness. All components were developed in a single reproducible Jupyter / Colab workflow.

Challenges we ran into

One major challenge was ensuring medical realism while working with synthetic data. We needed to carefully model correlations between risk factors, disease outcomes, and ECG patterns without introducing unrealistic bias.

Another challenge was balancing model performance with interpretability, especially in a healthcare context where trust and transparency are critical. Designing explainable outputs that remain clinically meaningful required thoughtful feature analysis and validation.

Accomplishments that we're proud of

1. Built a multimodal AI system combining tabular and ECG data.
2. Designed a risk score framework aligned with clinical decision-making.
3. Implemented explainable AI for patient-level insights.
4. Achieved strong predictive performance while maintaining model calibration.
5. Created a project that is reproducible, scalable, and ethically grounded.

What we learned

Through VitaBeat, we learned that successful healthcare AI is not just about maximizing accuracy—it’s about trust, interpretability, and usability.

We gained hands-on experience in:

1. Medical feature engineering.
2. Time-series deep learning.
3. Model calibration and error analysis.
4. Responsible and ethical AI design.

Most importantly, we learned how to translate machine learning outputs into clinically actionable insight.

What's next for VitaBeat

Future directions for VitaBeat include:

1. Validation using real-world ECG and clinical datasets.
2. True multimodal model fusion between ECG and tabular data.
3. Deployment as a lightweight web or mobile screening tool.
4. Bias evaluation across demographics.
5. Collaboration with clinicians for clinical feedback and refinement.

Our long-term vision is for VitaBeat to support accessible, early cardiovascular screening worldwide. Thank You.

Built With

• jupyter
• matplotlib
• notebook
• numpy
• pandas
• python
• scikit-learn
• scipy
• seaborn
• shap
• tensorflow