Random Forest, Gradient Boosting, and CoxNet with model comparison and variable importance
Machine Learning Survival Models combine the power of modern machine learning algorithms with traditional survival analysis. While classical methods like Cox regression are great, ML models can capture complex, non-linear relationships in your data that traditional methods might miss.
Think of it as upgrading from a basic calculator to a smart computer—ML models can automatically detect patterns, interactions between variables, and make more accurate predictions for complex datasets.
What it is: An ensemble of decision trees that vote on survival predictions
How it works: Creates hundreds of decision trees, each trained on a random subset of your data. Each tree makes a prediction, and the final result is the average of all trees.
Best for: Datasets with complex interactions between variables. Handles missing data well and automatically detects important features.
Example: Predicting life insurance mortality when age, health conditions, and lifestyle factors interact in complex ways (e.g., smoking + diabetes + age).
What it is: Sequential learning where each model corrects the errors of previous ones
How it works: Builds trees one at a time, where each new tree focuses on fixing the mistakes made by earlier trees. Like a student learning from their errors.
Best for: Maximum prediction accuracy. Often wins machine learning competitions. Great when you need the most precise survival estimates possible.
Example: Pricing high-value life insurance policies where small prediction errors can cost millions. Needs the highest accuracy possible.
What it is: Cox regression enhanced with machine learning regularization
How it works: Like traditional Cox regression but with automatic feature selection. It automatically removes unimportant variables and prevents overfitting.
Best for: Datasets with many variables where you want to identify which ones truly matter. Provides interpretable coefficients like traditional Cox models.
Example: Analyzing 50+ potential risk factors for policy lapse and automatically identifying the 10 most important ones.
Same as traditional survival analysis: you need time-to-event and status columns. ADaaS automatically detects these.
ADaaS prepares your data: handles missing values, encodes categorical variables, and scales numeric features—all automatically.
With one click, ADaaS trains Random Forest, Gradient Boosting, and CoxNet models simultaneously. No coding or parameter tuning needed.
ADaaS shows you which model performs best using C-index scores. Higher is better. Typically 0.7+ is good, 0.8+ is excellent.
See which factors matter most for survival predictions. For example: "Age is 3x more important than gender for predicting mortality."
Side-by-side C-index scores showing which model is most accurate for your specific dataset.
Generate survival curves for any individual based on their characteristics. "What's the 5-year survival probability for a 45-year-old smoker?"
ML models automatically find interactions (e.g., "smoking + diabetes together increases risk more than their individual effects combined").
| Aspect | Traditional Cox | ML Models |
|---|---|---|
| Complexity | Linear relationships only | ✓ Captures non-linear patterns |
| Interactions | Must specify manually | ✓ Detects automatically |
| Accuracy | Good for simple data | ✓ Better for complex data |
| Interpretability | ✓ Clear coefficients | Importance scores (less direct) |
| Best Use | Regulatory reporting, simple models | Maximum accuracy, complex datasets |
When pricing million-dollar policies, ML's extra accuracy can save significant money by better identifying low-risk vs high-risk applicants.
Predict outcomes for patients with multiple interacting health conditions where traditional models struggle.
Identify which policyholders are most likely to lapse and when, enabling targeted retention campaigns.
Automatically discover which of 50+ variables actually matter for your predictions, saving analysis time.
Upload your data and compare all three models automatically