document: Predicting prostate cancer recurrence via maximizing the concordance index In order to effectively use machine learning algorithms , e.g. , neural networks , for the analysis of survival data , the correct treatment of censored data is crucial . The concordance index ( CI ) is a typical metric for quantifying the predictive ability of a survival model . We propose a new algorithm that directly uses the CI as the objective function to train a model , which predicts whether an event will eventually occur or not . Directly optimizing the CI allows the model to make complete use of the information from both censored and non-censored observations . In particular , we approximate the CI via a differentiable function so that gradient-based methods can be used to train the model . We applied the new algorithm to predict the eventual recurrence of prostate cancer following radical prostatectomy . Compared with the traditional Cox proportional hazards model and several other algorithms based on neural networks and support vector machines , our algorithm achieves a significant improvement in being able to identify high-risk and low-risk groups of patients .
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