The effect of imputing missing clinical attribute values on training lung cancer survival prediction model performance View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2017-12-06

AUTHORS

Mohamed S. Barakat, Matthew Field, Aditya Ghose, David Stirling, Lois Holloway, Shalini Vinod, Andre Dekker, David Thwaites

ABSTRACT

According to the estimations of the World Health Organization and the International Agency for Research in Cancer, lung cancer is the most common cause of death from cancer worldwide. The last few years have witnessed a rise in the attention given to the use of clinical decision support systems in medicine generally and in cancer in particular. These can predict patients’ likelihood of survival based on analysis of and learning from previously treated patients. The datasets that are mined for developing clinical decision support functionality are often incomplete, which adversely impacts the quality of the models developed and the decision support offered. Imputing missing data using a statistical analysis approach is a common method to addressing the missing data problem. This work investigates the effect of imputation methods for missing data in preparing a training dataset for a Non-Small Cell Lung Cancer survival prediction model using several machine learning algorithms. The investigation includes an assessment of the effect of imputation algorithm error on performance prediction and also a comparison between using a smaller complete real dataset or a larger dataset with imputed data. Our results show that even when the proportion of records with some missing data is very high (> 80%) imputation can lead to prediction models with an AUC (0.68–0.72) comparable to those trained with complete data records. More... »

PAGES

16

References to SciGraph publications

  • 2009-11-19. Ensemble-based classifiers in ARTIFICIAL INTELLIGENCE REVIEW
  • 2010-01. Ensemble Methods in Data Mining in NONE
  • 2009-09-03. Pattern classification with missing data: a review in NEURAL COMPUTING AND APPLICATIONS
  • 2014-09-21. Techniques for dealing with incomplete data: a tutorial and survey in PATTERN ANALYSIS AND APPLICATIONS
  • 2008-01-29. A Markov chain Monte Carlo algorithm for multiple imputation in large surveys in ASTA ADVANCES IN STATISTICAL ANALYSIS
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    http://scigraph.springernature.com/pub.10.1007/s13755-017-0039-4

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    http://dx.doi.org/10.1007/s13755-017-0039-4

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    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/29255599


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