This preprint presents a reproducible machine-learning study of COVID-19 test outcome classification using symptom and testing-context features from a public 2020-2021 dataset. The study compares seven supervised learning algorithms and examines whether apparent model performance is maintained under stricter temporal validation. The original comparison used the first 1,000,000 records, while the corrected secondary analysis used all 5,861,480 records with separate development, validation and untouched temporal-holdout periods. Calibration and threshold selection were restricted to validation data. The findings show that random-split performance did not translate reliably to later records. In the corrected temporal holdout, ROC-AUC fell to 0.694 and precision to 0.136, demonstrating substantial temporal degradation. The manuscript is therefore presented as a reproducible validation-design and machine-learning case study, not as a clinically deployable diagnostic model. The source code and workflow are available at:https://github.com/Edy-King/covid-19-ml-workflow This manuscript has not been peer reviewed.
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