ArticleJournal of big data2021
A survey on missing data in machine learning.
Article in Journal of big data, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 181 papers, 2 of them syntheses that pooled it.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
181 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Identify the most appropriate imputation method for handling missing values in clinical structured datasets: a systematic review.BMC medical research methodology · 2024Pooled it
- Machine Learning for prediction of violent behaviors in schizophrenia spectrum disorders: a systematic review.Frontiers in psychiatry · 2024Pooled it
- Machine learning for missing data imputation in Alzheimer's research: predicting medial temporal lobe dynamic flexibility.Cognitive neurodynamics · 2026Article
- Data-Driven Materials Science for Energy-Sustainable Applications.Advanced materials (Deerfield Beach, Fla.) · 2026Review
- Continuous imputation of blood gas and metabolic panel laboratory values in the intensive care unit using machine learning.Journal of clinical monitoring and computing · 2026Article
- Population pharmacokinetics of dalbavancin: external validation, model averaging, and implications for precision dosing in prolonged therapy.Antimicrobial agents and chemotherapy · 2026Article
- Determinants and predictive modeling of long-acting reversible contraceptive use in Sub-Saharan Africa: evidence from DHS data using machine learning and association rule mining.AJOG global reports · 2026Article
- Energy Consumption and Carbon Emission Prediction of District Heating System in Residential Communities Based on SSA-LSTM Model.Sensors (Basel, Switzerland) · 2026Article
- Performance of the Pre-Eclampsia Integrated Estimate of Risk-Machine Learning (PIERS-ML) model in a Kenyan cohort of women with pre-eclampsia- a retrospective test derivation validation study.BMC pregnancy and childbirth · 2026Article
- AlzStack: Forecasting early-onset Alzheimer's with an explainable AI system using multiple data balancing techniques.Global epidemiology · 2026Article
- Machine learning-optimized composting strategies can enhance nutrient recycling and transform food system waste into a net carbon sink.Nature food · 2026Article
- Phenotype discovery and mortality prediction in sepsis-induced myocardial dysfunction: a deep learning and stratified modeling approach.BMC medical informatics and decision making · 2026Article
- Machine learning in mental health promotion for older adults: a scoping review.BMC geriatrics · 2026Article
- A leakage-controlled and SHAP driven machine learning framework for paediatric respiratory disease classification using Indian hospital EHR data.BMC medical informatics and decision making · 2026Article
- Development and validation of an early risk prediction model based on inflammatory and metabolically derived markers for early diagnosis of diabetic kidney disease.Scientific reports · 2026Article
- Machine learning-based prediction of tensile strength of glass fiber-reinforced polymer rebar under environmental conditions.Advances in structural engineering · 2026Article
- Equiflow: An open-source software package for evaluating changes in cohort composition.PLOS digital health · 2026Article
- Visit-level prediction of missed HIV appointments using machine learning and transformer-based models in Uganda.BMC medical informatics and decision making · 2026Article
- Digital Twins for Just-in-Time Adaptive Interventions (JITAIs): Framework for Optimizing and Continually Improving JITAIs.Journal of medical Internet research · 2026Article
- Machine learning for predicting climate change impacts onNew Zealand veterinary journal · 2026Article
121 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Machine learning has been the corner stone in analysing and extracting information from data and often a problem of missing values is encountered. Missing values occur because of various factors like missing completely at random, missing at random or missing not at random. All these may result from system malfunction during data collection or human error during data pre-processing. Nevertheless, it is important to deal with missing values before analysing data since ignoring or omitting missing values may result in biased or misinformed analysis. In literature there have been several proposals for handling missing values. In this paper, we aggregate some of the literature on missing data particularly focusing on machine learning techniques. We also give insight on how the machine learning approaches work by highlighting the key features of missing values imputation techniques, how they perform, their limitations and the kind of data they are most suitable for. We propose and evaluate two methods, the k nearest neighbor and an iterative imputation method (missForest) based on the random forest algorithm. Evaluation is performed on the Iris and novel power plant fan data with induced missing values at missingness rate of 5% to 20%. We show that both missForest and the k nearest neighbor can successfully handle missing values and offer some possible future research direction.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.