title: Drawing inferences from clinical studies with missing values using genetic algorithm.
abstract: 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.
authors.affiliation: Kongu Engineering College, Erode 638 052, Tamil Nadu, India.
authors.forename: R Devi
authors.initials: RD
authors.lastname: Priya
authors.affiliation: Kongu Engineering College, Erode 638 052, Tamil Nadu, India.
authors.forename: S
authors.initials: S
authors.lastname: Kuppuswami
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D002986:Clinical Trials as Topic / Q000379:methods*; D003198:Computer Simulation; D015340:Epidemiologic Research Design; D008957:Models, Genetic; D015233:Models, Statistical; D012107:Research Design; D018401:Sample Size
keywords: BGA; Bayesian genetic algorithms; MAR; MCAR; bioinformatics; clinical studies; continuous attributes; discrete attributes; missing at random; missing completely at random; missing values