An Analysis of Missing Data Treatment Methods and Their Application to Health Care Dataset

Liu, Peng; El‐Darzi, Elia; Lei, Lei; Vasilakis, Christos; Chountas, Panagiotis; Huang, Wei

doi:10.1007/11527503_69

Cited by 13 publications

(9 citation statements)

References 5 publications

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“…However, low applicability raises the issue of dealing with missing data, increasingly discussed in the literature [ 29 ]. In order to avoid loss of power we applied a combination of two popular methods: case deletion and constant replacement [ 30 ].…”

Section: Discussionmentioning

confidence: 99%

Translation of the Neck Disability Index and validation of the Greek version in a sample of neck pain patients

Trouli

Vernon

Kakavelakis

et al. 2008

BMC Musculoskelet Disord

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Background: Neck pain is a highly prevalent condition resulting in major disability. Standard scales for measuring disability in patients with neck pain have a pivotal role in research and clinical settings. The Neck Disability Index (NDI) is a valid and reliable tool, designed to measure disability in activities of daily living due to neck pain. The purpose of our study was the translation and validation of the NDI in a Greek primary care population with neck complaints.

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Section: Discussionmentioning

confidence: 99%

Translation of the Neck Disability Index and validation of the Greek version in a sample of neck pain patients

Trouli

Vernon

Kakavelakis

et al. 2008

BMC Musculoskelet Disord

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“…A recent application in which incomplete data appear is biology research with DNA microarrays, where the gene data may be missing due to various reasons such as scratch on the slide or contaminated samples [20,21]. Missing values are common in medical diagnosis, e.g., a medical practitioner may not order a test whose outcome appears certain or not relevant to the diagnosis, or a feature can be missing because it proved to be difficult/harmful to measure [22][23][24][25].…”

Section: Missing Data Mechanismsmentioning

confidence: 99%

Pattern classification with missing data: a review

García-Laencina

Sancho‐Gómez

Figueiras-Vidal

2009

Neural Comput & Applic

649

388

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Pattern classification has been successfully applied in many problem domains, such as biometric recognition, document classification or medical diagnosis. Missing or unknown data are a common drawback that pattern recognition techniques need to deal with when solving real-life classification tasks. Machine learning approaches and methods imported from statistical learning theory have been most intensively studied and used in this subject. The aim of this work is to analyze the missing data problem in pattern classification tasks, and to summarize and compare some of the well-known methods used for handling missing values.

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“…Imputation technique is one of the widely used missing data treatment methods [11]. The basic idea of Naive Bayesian Imputation (NBI) is first to define the feature to be imputed, called 'imputation feature' and then construct the NBC using the imputation feature as the class feature.…”

Section: Nbi Modelsmentioning

confidence: 99%