Drawing inferences from clinical studies with missing values using genetic algorithm

Missing data problem degrades the statistical power of any analysis made in clinical studies. To infer valid results from such studies, suitable method is required to replace the missing values. There is no method which can be universally applicable for handling missing values and the main objective...

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Bibliographic Details
Published in:International journal of bioinformatics research and applications Vol. 10; no. 6; p. 613
Main Authors: Priya, R Devi, Kuppuswami, S
Format: Journal Article
Language:English
Published: Switzerland 2014
Subjects:
Algorithms
Bayes Theorem
Clinical Trials as Topic - methods
Computer Simulation
Epidemiologic Research Design
Models, Genetic
Models, Statistical
Research Design
Sample Size
Bayesian genetic algorithms
MCAR
discrete attributes
bioinformatics
clinical studies
continuous attributes
missing at random
missing completely at random
BGA
MAR
missing values
ISSN:1744-5485
Online Access:Get more information
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Summary:Missing data problem degrades the statistical power of any analysis made in clinical studies. To infer valid results from such studies, suitable method is required to replace the missing values. There is no method which can be universally applicable for handling missing values and the main objective of this paper is to introduce a common method applicable in all cases of missing data. In this paper, Bayesian Genetic Algorithm (BGA) is proposed to effectively impute both missing continuous and discrete values using heuristic search algorithm called genetic algorithm and Bayesian rule. BGA is applied to impute missing values in a real cancer dataset under Missing At Random (MAR) and Missing Completely At Random (MCAR) conditions. For both discrete and continuous attributes, the results show better classification accuracy and RMSE% than many existing methods.
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ISSN:1744-5485
DOI:10.1504/IJBRA.2014.065245