Feature selection through validation and un-censoring of endovascular repair survival data for predicting the risk of re-intervention

BMC Medical Informatics and Decision Making

Research Article

Xianghong Ma¹  Omneya Attallah²  Peter J. E. Holt³  Alan Karthikesalingam³  Matthew M. Thompson³  Rob Sayers⁴  Eddie C. Choke⁵  Matthew J. Bown⁶ 

[1] School of Engineering and Applied Science, Aston University, B4 7ET, Birmingham, UK;School of Engineering and Applied Science, Aston University, B4 7ET, Birmingham, UK;Department of Electronics and Communications, College of Engineering and Technology, Arab Academy for Science and Technology, Alexandria, Egypt;St George’s Vascular Institute, SW17 0QT, London, UK;St George’s Vascular Institute, St George’s University Hospitals NHS Foundation Trust, Blackshaw Road, SW17 0QT, London, UK;Vascular Surgery Group, Robert Kilpatrick Clinical Sciences Building, Leicester Royal Infirmary, University of Leicester, LE2 7LX, Leicester, UK;Vascular Surgery Group, University of Leicester, Leicester, UK;

关键词: Survival analysis;    Censoring;    Feature selection;    Model selection;    Factor analysis;    Cox’s hazard proportional model;    Endovascular aortic aneurysm repair;   

DOI  :  10.1186/s12911-017-0508-3

 received in 2016-07-05, accepted in 2017-07-24,  发布年份 2017

来源: Springer

【 摘 要 】

BackgroundFeature selection (FS) process is essential in the medical area as it reduces the effort and time needed for physicians to measure unnecessary features. Choosing useful variables is a difficult task with the presence of censoring which is the unique characteristic in survival analysis. Most survival FS methods depend on Cox’s proportional hazard model; however, machine learning techniques (MLT) are preferred but not commonly used due to censoring. Techniques that have been proposed to adopt MLT to perform FS with survival data cannot be used with the high level of censoring. The researcher’s previous publications proposed a technique to deal with the high level of censoring. It also used existing FS techniques to reduce dataset dimension. However, in this paper a new FS technique was proposed and combined with feature transformation and the proposed uncensoring approaches to select a reduced set of features and produce a stable predictive model.MethodsIn this paper, a FS technique based on artificial neural network (ANN) MLT is proposed to deal with highly censored Endovascular Aortic Repair (EVAR). Survival data EVAR datasets were collected during 2004 to 2010 from two vascular centers in order to produce a final stable model. They contain almost 91% of censored patients. The proposed approach used a wrapper FS method with ANN to select a reduced subset of features that predict the risk of EVAR re-intervention after 5 years to patients from two different centers located in the United Kingdom, to allow it to be potentially applied to cross-centers predictions. The proposed model is compared with the two popular FS techniques; Akaike and Bayesian information criteria (AIC, BIC) that are used with Cox’s model.ResultsThe final model outperforms other methods in distinguishing the high and low risk groups; as they both have concordance index and estimated AUC better than the Cox’s model based on AIC, BIC, Lasso, and SCAD approaches. These models have p-values lower than 0.05, meaning that patients with different risk groups can be separated significantly and those who would need re-intervention can be correctly predicted.ConclusionThe proposed approach will save time and effort made by physicians to collect unnecessary variables. The final reduced model was able to predict the long-term risk of aortic complications after EVAR. This predictive model can help clinicians decide patients’ future observation plan.

【 授权许可】

CC BY   
© The Author(s). 2017