Why Your Healthcare AI is Failing: A Deep Dive into Stacked Ensembles and the Accuracy Paradox🩺

We have all been there. You train a model, the validation accuracy hits 98% , and you start planning the production rollout. Then you look at the Confusion Matrix and realize the truth: your model did not actually learn anything. It simply predicted "Healthy" for every single patient because 98% of your dataset was healthy. In healthcare, this is not just a "bad model." It is a dangerous one. If you are building a system to detect Hypertension , an accuracy score that misses the 2% of at-risk patients is a total failure. In a clinical setting, an undetected case is a missed opportunity for life-saving intervention. As a Data and Technology Program Lead, I have spent my career at the intersection of healthcare and predictive modeling. Solving this "Accuracy Paradox" requires more than just better algorithms; it requires a fundamental shift in how we handle data geometry and model architecture. Here is the deep technical breakdown of how I tackled class imbalance and high-dimensional med