Supervised machine learning-based bias risk of prognostic models for total knee or hip arthroplasty patients: A systematic review

Abstract

Almost all supervised ML models have the potential for a high bias risk. Factors contributing to a high bias risk include inadequate sample size, missing data during recruitment, model overfitting, and limited external validation. Adhering to strict standards and implementing comprehensive improvements when constructing prognosis models using supervised ML is crucial.

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