A Research Classification for Long COVID: Symptom Frequency and Severity Improve Accuracy of Machine Learning Models

Summary Long COVID definitions based solely on symptom occurrence may reduce diagnostic specificity. Machine‐learning models incorporating symptom frequency and severity outperformed occurrence‐only models. Composite scoring achieved 90.12% accuracy compared with 88.73% for occurrence‐based scoring. Composite models required fewer predictive symptoms, indicating greater efficiency. Measuring symptom burden improves the precision of research classification for Long COVID.