The 99% Problem: When a Stroke Benchmark Looks Ready Before It Is
TL;DR for operators A model reporting 99% accuracy creates an obvious decision pressure: should the team fund integration, start clinical workflow design, or treat the experiment as essentially solved? This paper is a useful example of why that decision cannot be made from the headline number alone. Its main results table reports a Stacking Classifier at 99.81% accuracy, Random Forest at 99.52%, and Bagging at 99.45%.1 But those results come from one public Kaggle dataset after the original 5,110 records—249 stroke-positive and 4,861 stroke-negative—were reduced to a balanced working dataset containing 249 observations in each class. ...