Simulation Study of Feature Selection on Survival Least Square Support Vector Machines with Application to Health Data
One of semi parametric survival model commonly used is Cox Proportional Hazard Model (Cox PHM) that has some conditions must be satisfied, one of them is proportional hazard assumption among the category at each predictor. Unfortunately, the real case cannot always satisfy this assumption. One alternative model that can be employed is non-parametric approach using Survival Least Square-Support Vector Machine (SURLS-SVM). Meanwhile, the SURLS-SVM cannot inform which predictors are significant like the Cox PHM can do. To overcome this issue, the feature selection using backward elimination is employed by means of c-index increment. This paper compares two approaches, i.e. Cox PHM and SURLS-SVM, using c-index criterion applied on simulated and clinical data. The empirical results inform that the c-index of SURLS-SVM is higher than Cox PHM on both datasets. Furthermore, the simulation study is repeated 100 times. The simulation results show that the non-relevant predictors are often included in the model because the effect of confounding. For the application on clinical data (cervical cancer), the feature selection yields nine relevant predictors out of twelve predictors. The three predictors among the nine relevant predictors in SURLS-SVM are the significant predictors in Cox PHM.
KeywordsSurvival Least square SVM Features selection Simulation Cervical cancer
Authors thank to the reviewers for their advices. This research is supported by fundamental research scheme (PDUPT) in ITS number 871/PKS/ITS/2018 financed by DRPM DIKTI, Indonesian Ministry of Research, Technology and Higher Education (number 128/SP2H/PTNBH/DRPM/2018).
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