ADM Prediction (Made Before Running Models)
Predicted winner: ML. Health trajectories are individual-specific. Population curves capture the average trend but miss individual slopes. Linear extrapolation works short-term but diverges at longer horizons because health transitions are nonlinear (disease onset creates step changes).
Expected: ML wins by 10–20% RMSE at longer horizons (8+ years). Actual: 15–19% RMSE improvement, growing with prediction horizon. Prediction confirmed.
Health care systems plan resources based on population averages — but individuals diverge dramatically from the mean. Some maintain excellent self-rated health into their 80s while others experience rapid decline in their 50s. Predicting which trajectory a person will follow from their early health history is the clinical challenge.
Domain Model
Population age curves: mean self-rated health at each age, adjusted for sex. Predicts the average trajectory.
ML Model
GradientBoosting trained on first 3 waves: health ratings, BMI trend, depression, disease burden, lifestyle.
Trajectory Types
Stable (35%), gradual decline (30%), rapid decline (15%), recovery (10%), volatile (10%).
Prediction Horizons
2, 4, 6, 8, 10, and 12 years. ML advantage grows with horizon length.
The domain model uses population-level age curves from the HRS dataset. For a given person's current age and sex, it looks up the average self-rated health (1=excellent to 5=poor) and projects it forward using the population mean trajectory. This is the simplest reasonable baseline — it knows that health declines with age but treats everyone the same.
The ML model is a GradientBoosting regressor trained on the first 3 waves of each participant's data. Features include: baseline health rating, BMI trajectory, activity level, age, disease count, depression score, education, and smoking history. It predicts health at horizons of 2 through 12 years.
Both models are evaluated on held-out test sets using RMSE. The key finding: the ML advantage grows with prediction horizon. At 2 years, both methods are similar because individual differences haven't had time to manifest. At 12 years, ML's advantage reaches 15–19%.
The population mean health trajectory shows a gentle decline over 12 waves. But the interquartile range (shaded bands) reveals massive individual variation. The domain model predicts only the center line; the ML model captures who falls where within the spread.
Both models predict self-rated health at six horizons. The domain model uses population averages; the ML model uses individual features from early waves. The ML advantage widens as the horizon grows — individual patterns matter more over longer periods.
The ML model improves over the domain baseline across all trajectory types, but the improvement is largest for atypical trajectories — recovery and rapid decline — exactly where personalized insight matters most.
Baseline health is the single strongest predictor — where you start determines where you're likely to go. But BMI trajectory, activity level, and disease burden add substantial predictive value beyond demographics alone.
Self-rated health as outcome: The predicted variable is self-rated health (1-5 scale), which is subjective and influenced by mood, expectations, and cultural norms. Objective health measures (biomarkers, functional tests) might show different domain-vs-ML patterns.
First 3 waves as features: The ML model uses the first 3 waves (~6 years) to predict future health. People who drop out before wave 3 are excluded, creating survivorship bias toward healthier individuals.
RMSE improvement vs simpler baselines: The 15-19% RMSE improvement is over population age curves. A comparison to cubic splines or individual-level linear trends would provide a more rigorous baseline for the ML advantage.
Trajectory types are post-hoc: The 5 trajectory types (stable, gradual decline, rapid decline, recovery, volatile) are clustering labels applied after observing all waves, not prospectively defined categories.
- ML advantage grows with prediction horizon — 15–19% improvement at 8–12 years. At shorter horizons, population averages are nearly as good because individual differences haven't had time to manifest.
- Recovery and rapid decline trajectories benefit most — these are the hardest to predict but the most clinically important. The people who defy the average are exactly who we need to identify early.
- Baseline health is the strongest predictor — accounting for 28% of feature importance. But BMI trajectory, activity level, and disease burden add substantial predictive value beyond where you start.