  One of the most pressing challenges facing the fusion community is adequately
mitigating or, even better, avoiding disruptions of tokamak plasmas. However,
before this can be done, disruptions must first be predicted with sufficient
warning time to actuate a response. The established field of survival analysis
provides a convenient statistical framework for time-to-event (i.e.
time-to-disruption) studies. This paper demonstrates the integration of an
existing disruption prediction machine learning algorithm with the Kaplan-Meier
estimator of survival probability. Specifically discussed are the implied
warning times from binary classification of disruption databases and the
interpretation of output signals from Random Forest algorithms trained and
tested on these databases. This survival analysis approach is applied to both
smooth and noisy test data to highlight important features of the survival and
hazard functions. In addition, this method is applied to three Alcator C-Mod
plasma discharges and compared to a threshold-based scheme for triggering
alarms. In one case, both techniques successfully predict the disruption;
although, in another, neither warns of the impending disruption with enough
time to mitigate. For the final discharge, the survival analysis approach could
avoid the false alarm triggered by the threshold method. Limitations of this
analysis and opportunities for future work are also presented.
