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Can machine‐learning algorithms replace conventional statistics?

Wong et al. [1] evaluate 19 clinical variables (training data) and three supervised machine-learning algorithms to predict early biochemical recurrence after robotassisted prostatectomy. They further compare the areas under the curve (AUCs) resulting from these algorithms with the AUC of a conventional Cox regression model and conclude that the machine-learning algorithms can produce accurate disease prognosis, perhaps better than a traditional Cox regression model. As the authors state, predictive models have the potential to better individualize care to patients at highest risk of prostate cancer recurrence and progression.