Drawing inferences from clinical studies with missing values using genetic algorithm.

Q4 Health Professions

International Journal of Bioinformatics Research and Applications Pub Date : 2014-01-01 DOI:10.1504/IJBRA.2014.065245

R Devi Priya, S Kuppuswami

引用次数: 13

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.

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利用遗传算法从缺失值的临床研究中进行推断。

缺失数据问题降低了临床研究中任何分析的统计能力。为了从这些研究中推断出有效的结果，需要合适的方法来替换缺失的值。没有一种方法可以普遍适用于处理缺失值，本文的主要目的是介绍一种适用于所有缺失数据情况的通用方法。本文提出了一种基于遗传算法和贝叶斯规则的启发式搜索算法——贝叶斯遗传算法(BGA)来有效地对缺失的连续值和离散值进行归算。将BGA应用于随机缺失(missing At Random, MAR)和完全随机缺失(missing完全At Random, MCAR)条件下真实癌症数据集的缺失值估算。对于离散和连续属性，结果表明分类精度和RMSE%都优于现有的许多方法。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

International Journal of Bioinformatics Research and Applications Health Professions-Health Information Management

CiteScore

0.60

自引率

0.00%

发文量

期刊介绍： Bioinformatics is an interdisciplinary research field that combines biology, computer science, mathematics and statistics into a broad-based field that will have profound impacts on all fields of biology. The emphasis of IJBRA is on basic bioinformatics research methods, tool development, performance evaluation and their applications in biology. IJBRA addresses the most innovative developments, research issues and solutions in bioinformatics and computational biology and their applications. Topics covered include Databases, bio-grid, system biology Biomedical image processing, modelling and simulation Bio-ontology and data mining, DNA assembly, clustering, mapping Computational genomics/proteomics Silico technology: computational intelligence, high performance computing E-health, telemedicine Gene expression, microarrays, identification, annotation Genetic algorithms, fuzzy logic, neural networks, data visualisation Hidden Markov models, machine learning, support vector machines Molecular evolution, phylogeny, modelling, simulation, sequence analysis Parallel algorithms/architectures, computational structural biology Phylogeny reconstruction algorithms, physiome, protein structure prediction Sequence assembly, search, alignment Signalling/computational biomedical data engineering Simulated annealing, statistical analysis, stochastic grammars.