What does "survivalsvm" predict?

user_11645594 0
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I'm trying to develop a predictive model using cancer survival data and used the R package survivalsvm which uses SVM method. After running the following code i got some results but finding it difficult to interpret it. I know that in Cox regression it predicts the Cumulative Hazard Function, but is it the same in survivalsvm? I ran both Cox and survivalsvm models and the results are quite different:

smodel_svm = survivalsvm(Surv(time, outcome) ~ radius.mean + tumor.size, data=training_set, gamma.mu = 1)
pred_test_svm = predict(smodel_svm, test_set)
summary(pred_test_svm)
survival-analysis svm machine-learning r
2 Answers

The difference might be because you're using default parameters that is using type="regression" that uses the regression approach as described in this paper.

In summary, the authors(Van Belle et al. ) propose a different approach(MODEL 2 and MODEL 3) that essentially uses a Cox model but with both regression and ranking constraints.

Note however that the authors concluded:

Comparison of model 2 with the coxmodel revealed no significant differences in performance. The advantage of model 2 above cox model lies in the easy extension towards non-linear models without the need to check non-linearities in the variables before modelling

From the function's docs(focusing on the parameter type):

The following denotations are used for the models implemented:

'regression' referring to the regression approach, named SVCR in Van Belle et al. (2011b),

'vanbelle1' according to the first version of survival surpport vector machines based on ranking constraints, named RANKSVMC by Van Belle et al. (2011b),

'vanbelle2' according to the second version of survival surpport vector machines based on ranking constraints like presented in model1 by Van Belle et al. (2011b) and

user_103237980

Predictions from survivalsvm have to be interpreted as ranks. The way in survival svm is to be able to predict ranks among individuals to estimate which patient, for example, should be handled earlier than others. See also fouodo et al. (2018) for more details about package usage in R.

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