ArticlePloS one2021
Evaluating the impact of multivariate imputation by MICE in feature selection.
Article in PloS one, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 40 papers.
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Who cites it
40 citing papers in PubMed.
- Capturing silent oxidative stress in early Alzheimer's disease: prediction of CSF biomarkers from sleep qEEG data.GeroScience · 2026Article
- A comparative analysis in a clinical cohort: multiple imputation by chained equations and a novel super learner-based imputation approach.BMC medical research methodology · 2026Article
- Multi task learning based early prediction model for antibiotic resistance using multi institutional cohort data.Scientific reports · 2026Article
- Interpretable machine learning-based predictive model for fall risk in older adults receiving maintenance hemodialysis.Frontiers in medicine · 2026Article
- Gray matter microstructure from in vivo diffusion magnetic resonance imaging reflects post mortem neuropathology severity and clinical progression of Alzheimer's disease.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2026Article
- Risk factors of decisional conflict among people living with chronic pain identified through a pan-Canadian survey.Scientific reports · 2025Article
- Predicting mortality in elderly chronic kidney disease patients using algorithmic risk assessment: a Southeast Asian perspective.BMC nephrology · 2025Article
- Predicting hospital admissions, ICU utilization, and prolonged length of stay among febrile pediatric emergency department patients using incomplete and imbalanced electronic health record (EHR) data strategies.International journal of medical informatics · 2025Article
- Modelling annual prevalence of tobacco consumption in Spain, 1991-2020.European journal of public health · 2025Article
- Preserving Informative Presence: How Missing Data and Imputation Strategies Affect the Performance of an AI-Based Early Warning Score.Journal of clinical medicine · 2025Article
- Machine Learning to Assist in Managing Acute Kidney Injury in General Wards: Multicenter Retrospective Study.Journal of medical Internet research · 2025Article
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- Construction of a prognostic prediction model for colorectal cancer based on 5-year clinical follow-up data.Scientific reports · 2025Article
- Retinal microvascular density and inner thickness in Alzheimer's disease and mild cognitive impairment.Frontiers in aging neuroscience · 2025Article
- Serum protein α-klotho mediates the association between lead, mercury, and kidney function in middle-aged and elderly populations.Environmental health and preventive medicine · 2025Article
- Handling missing data of using the XGBoost-based multiple imputation by chained equations regression method.Frontiers in artificial intelligence · 2025Article
- Article
- The mediating and joint effects of depression in the association between cardiovascular health and frailty in middle-aged and elderly people: evidence from NHANES.Frontiers in psychiatry · 2025Article
- Prediction models for COVID-19 disease outcomes.Emerging microbes & infections · 2024Article
- A novel MissForest-based missing values imputation approach with recursive feature elimination in medical applications.BMC medical research methodology · 2024Article
Corrections and comments
- Erratum issued
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Handling missing values is a crucial step in preprocessing data in Machine Learning. Most available algorithms for analyzing datasets in the feature selection process and classification or estimation process analyze complete datasets. Consequently, in many cases, the strategy for dealing with missing values is to use only instances with full data or to replace missing values with a mean, mode, median, or a constant value. Usually, discarding missing samples or replacing missing values by means of fundamental techniques causes bias in subsequent analyzes on datasets.
aimDemonstrate the positive impact of multivariate imputation in the feature selection process on datasets with missing values.
resultsWe compared the effects of the feature selection process using complete datasets, incomplete datasets with missingness rates between 5 and 50%, and imputed datasets by basic techniques and multivariate imputation. The feature selection algorithms used are well-known methods. The results showed that the datasets imputed by multivariate imputation obtained the best results in feature selection compared to datasets imputed by basic techniques or non-imputed incomplete datasets.
conclusionsConsidering the results obtained in the evaluation, applying multivariate imputation by MICE reduces bias in the feature selection process.
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