{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Attallah O"],"funding":["National Institute for Health Research (NIHR)"],"pagination":["115"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC5543447"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["17(1)"],"pubmed_abstract":["<h4>Background</h4>Feature 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 "],"journal":["BMC medical informatics and decision making"],"pubmed_title":["Feature selection through validation and un-censoring of endovascular repair survival data for predicting the risk of re-intervention."],"pmcid":["PMC5543447"],"funding_grant_id":["NIHR-CS-011-008"],"pubmed_authors":["Karthikesalingam A","Sayers R","Attallah O","Choke EC","Ma X","Holt PJE","Bown MJ","Thompson MM"],"additional_accession":[]},"is_claimable":false,"name":"Feature selection through validation and un-censoring of endovascular repair survival data for predicting the risk of re-intervention.","description":"<h4>Background</h4>Feature 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 ","dates":{"release":"2017-01-01T00:00:00Z","publication":"2017 Aug","modification":"2025-06-01T01:34:01.442Z","creation":"2025-06-01T01:34:01.442Z"},"accession":"S-EPMC5543447","cross_references":{"pubmed":["28774329"],"doi":["10.1186/s12911-017-0508-3"]}}