Cardio Lens

Cardio-Lens: Democratizing heart health. Our interpretable AI screens populations and diagnoses patients, turning complex data into life-saving actions

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🫀 Cardio-Lens: Democratizing Heart Health — Project Story

💡 Inspiration

Cardiovascular disease (CVD) kills 17.9 million people every year — more than cancer, diabetes, and respiratory diseases combined. Yet early detection remains locked behind expensive clinical tests that most of the world can't access.

We asked ourselves one question:

What if a smartwatch could be the first line of defence?

The idea was simple but ambitious — build a Two-Tier AI System that mirrors how healthcare actually works:

  1. A mass screening model using data that wearable devices already collect (blood pressure, weight, activity level)
  2. A clinical model that kicks in only when the screening flags a risk, using data from actual medical tests (ECG readings, chest pain type, exercise angina)

This tiered approach means that billions of people with a wearable device could get an initial risk assessment — and only those flagged would need the clinical follow-up. That's the democratization we were after.

🧠 What We Learned

The Math Behind the Models

Both tiers use a Random Forest Classifier, an ensemble of decision trees that votes on the final prediction. The predicted probability for class $c$ is:

$$ P(c \mid \mathbf{x}) = \frac{1}{T} \sum_{t=1}^{T} P_t(c \mid \mathbf{x}) $$

where $T$ is the number of trees (150 for Tier 1, 200 for Tier 2) and $P_t$ is the probability estimate from tree $t$.

Feature Engineering Matters More Than Fancy Algorithms

One of our biggest "aha" moments was realizing that BMI — a derived feature — significantly boosted Tier 1 accuracy. We computed it as:

$$ \text{BMI} = \frac{\text{weight (kg)}}{\left(\frac{\text{height (cm)}}{100}\right)^2} $$

The raw dataset stored age in days, so we also had to convert:

$$ \text{age_years} = \left\lfloor \frac{\text{age_days}}{365.25} \right\rfloor $$

These small transformations taught us that domain knowledge + simple math often outperforms blindly throwing data at a neural network.

Data Cleaning is Half the Battle

The Tier 1 dataset (cardio_base.csv) contained 70,000 records, but many had physiologically impossible blood pressure values (e.g., systolic BP of 16000!). We applied clinically informed filters:

$$ 90 \leq \text{ap_hi} \leq 200 \quad \text{and} \quad 50 \leq \text{ap_lo} \leq 140 $$

After cleaning, we retained ~68,551 records — a 2% loss that dramatically improved model reliability.

Explainability is Non-Negotiable in Healthcare AI

We learned that in medical AI, showing why a prediction was made is just as important as the prediction itself. Our Tier 2 model exposes its feature importance scores, letting users (and doctors) see exactly which clinical factors drove the result — making it an Explainable AI (XAI) system, not a black box.

🔨 How We Built It

Architecture

┌──────────────────────────────────────────────────────────┐
│                     Streamlit Frontend                   │
│  ┌────────────┐ ┌──────────┐ ┌──────────┐ ┌───────────┐ │
│  │  The Pitch  │ │  Tier 1  │ │  Tier 2  │ │Health Twin│ │
│  └────────────┘ └──────────┘ └──────────┘ └───────────┘ │
├──────────────────────────────────────────────────────────┤
│                     backend.py                           │
│  ┌─────────────────────┐  ┌─────────────────────┐       │
│  │ load_and_preprocess  │  │   predict_tier1()   │       │
│  │    _tier1() / _tier2 │  │   predict_tier2()   │       │
│  │   train_tier1_model  │  │ simulate_bp_reduc() │       │
│  │   train_tier2_model  │  │                     │       │
│  └─────────────────────┘  └─────────────────────┘       │
├──────────────────────────────────────────────────────────┤
│   dataset/cardio_base.csv     dataset/heart_processed.csv│
│        (70k records)                (918 records)        │
└──────────────────────────────────────────────────────────┘

Tech Stack

Layer Technology Why We Chose It
Frontend Streamlit Rapid prototyping with gorgeous custom CSS — no React/JS needed
ML Models scikit-learn (Random Forest) Interpretable, fast to train, works great on tabular data
Data Pandas + NumPy Industry-standard for data wrangling and numerical ops
Visualizations Altair Declarative, interactive charts that respond to user input

The Two-Tier Pipeline

Tier 1 — The "Watch" Model (Mass Screening)

  • Trained on 70k+ records from a population cardiovascular dataset
  • Uses 12 features: age, gender, height, weight, BMI, systolic/diastolic BP, cholesterol, glucose, smoking, alcohol, physical activity
  • Hyperparameters: n_estimators=150, max_depth=12, min_samples_leaf=10
  • Includes a BP Simulator — an interactive slider that shows how reducing your systolic BP changes your risk in real-time, powered by running the model across a range:

$$ \text{risk}(bp) = P(\text{CVD} = 1 \mid \text{ap_hi} = bp, \ldots) \quad \forall \ bp \in [\text{target}, \text{current}] $$

Tier 2 — The Clinical Model (Doctor's Diagnosis)

  • Trained on 918 clinical records with 15 one-hot encoded features
  • Includes ECG readings, chest pain type, exercise-induced angina, ST slope
  • Hyperparameters: n_estimators=200, max_depth=10, min_samples_leaf=5
  • Outputs a feature importance chart for full explainability

🧬 The Health Twin Simulator — Our Unique Feature

This is the feature we're most proud of. No other heart disease app does this.

The simulator lets a user create a "Future Healthy Self" by setting health goals (lower BP, lose weight, quit smoking, exercise more). The AI then runs 22 parallel predictions — 11 years × 2 scenarios (current trajectory vs. healthy trajectory) — to build a side-by-side 10-Year Risk Trajectory.

The risk at each future year $y$ is modeled as:

$$ R_{\text{current}}(y) = P!\left(\text{CVD} \mid \text{age} + y,\; \text{current features}\right) $$

$$ R_{\text{healthy}}(y) = P!\left(\text{CVD} \mid \text{age} + y,\; \text{goal features}\right) $$

The "Years of Aging Reversed" metric converts the risk reduction into something intuitive — telling a user, for example, "Your heart will be 7 years younger after these changes."

🧗 Challenges We Faced

1. Noisy, Real-World Data

The Tier 1 dataset had extreme outliers — blood pressure values of 16,000 mmHg, negative ages, and heights of 250+ cm. We spent significant time designing robust filters that removed noise without accidentally discarding edge-case patients.

2. Class Imbalance

Both datasets had near-balanced classes (~50/50), which is unusual in medical datasets. While this simplified training, it made us question data collection methodology and iterate carefully with stratify=y in our train-test splits to preserve the ratio.

3. The "Two Dataset" Problem

Our two tiers used completely different datasets with zero overlapping features. This meant we couldn't build a single unified model — we had to design a system where the two models work sequentially but independently: the screening model acts as a gate, and the clinical model provides the definitive diagnosis.

4. Making AI Output Feel Human

Raw probability scores like 0.7341 mean nothing to a non-technical user. We spent a lot of time on UX — converting probabilities to percentage-based risk scores, color-coded cards (🟢 Low / 🟡 Moderate / 🔴 High), and the Health Twin's "Years of Aging Reversed" metaphor.

5. Performance at Scale

The BP Simulator runs the model across potentially 100+ blood pressure values in real-time as the user moves a slider. We used @st.cache_resource to avoid retraining models on every page load and vectorized predictions where possible to keep the experience snappy.

📊 Results

Metric Tier 1 (Screening) Tier 2 (Clinical)
Dataset Size ~68,551 records 918 records
Features 12 (wearable-level) 15 (clinical-level)
Accuracy ~72% ~87%
Use Case Mass population screening Hospital-grade diagnosis

While 72% accuracy for Tier 1 may seem modest, it's important to note that this model uses only wearable-level data — no blood tests, no ECGs, no clinical visits. For a tool that could run on a smartwatch and screen millions of people, a 72% early warning signal is a powerful first step.

🚀 What's Next

  • Deep Learning Upgrade: Replace Random Forest with a gradient-boosted ensemble (XGBoost/LightGBM) for potential accuracy gains
  • Real Wearable Integration: Connect with Apple HealthKit / Google Fit APIs for live data
  • Federated Learning: Train across hospital datasets without sharing patient data
  • Longitudinal Tracking: Let users log predictions over time and track their health journey

"The best time to check your heart health was 10 years ago. The second best time is now."

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