Devlog 1: First major milestone!

I just reached my first major milestone in building a full ML pipeline on the 2026 PhysioNet Challenge sleep dataset! Since it’s a relatively new dataset, I decided to train a model and predict cognitive impairment from overnight sleep recordings.

It took several hours to finish up the presentation and organize the key results. Initially, the baseline model showed promise (92% accuracy). However, it was basically guessing “no impairment” and hoping for the best, since there was only around 8% prevalence of cognitive impairment across all patients.

When I fixed the class imbalance and model setup, the real score was 0.80.What’s cool is that arousal frequency looked like the top predictor across two separate runs, and I thought it was a real finding, but when I tested it across 20 random seeds, it completely fell apart.

Of the full 75 features, the results indicate that only age and BMI really mattered, even though the model performed best after only seeing the top 10 features. Interestingly, those extra, noisy features actually did something: they degraded the model’s performance.