ArticleKnowledge-based systems2022
Missing Value Estimation using Clustering and Deep Learning within Multiple Imputation Framework.
Article in Knowledge-based systems, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 1 of them a synthesis that pooled it.
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Who cites it
20 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Machine learning-based risk predictive model for postoperative refractures in patients with osteoporotic vertebral compression fractures: a systematic review and critical appraisal.BMC musculoskeletal disorders · 2026Pooled it
- A novel average treatment effect estimation approach for integrated incomplete datasets, with application in childhood anemia.BMC medical research methodology · 2026Article
- Higher carbohydrate quality, but not quantity, is associated with lower lung cancer risk in a population-based prospective cohort study.Frontiers in nutrition · 2026Article
- Machine learning models for predicting new-onset diabetes following acute pancreatitis using real-world data.BMJ public health · 2026Article
- SysML: adaptive recommendation system for heterogeneous biomedical data preprocessing and modeling workflows.Briefings in bioinformatics · 2025Article
- Implicit bias in ICU electronic health record data: measurement frequencies and missing data rates of clinical variables.BMC medical informatics and decision making · 2025Article
- The analysis of sculpture image classification in utilization of 3D reconstruction under K-means+.Scientific reports · 2025Article
- Cafe: Improved Federated Data Imputation by Leveraging Missing Data Heterogeneity.IEEE transactions on knowledge and data engineering · 2025Article
- Personalized Health Prediction AI Models Using Transfer Learning and Strategic Overfitting on Wearable Device Data.Journal of medical systems · 2025Article
- Gestational and Early Childhood Exposure to Barium and Cardiometabolic Risk in Preschoolers: Findings From the Ma'anshan Birth Cohort Study in China.Journal of the American Heart Association · 2025Article
- Handling missing data of using the XGBoost-based multiple imputation by chained equations regression method.Frontiers in artificial intelligence · 2025Article
- Machine and Deep Learning Models for Hypoxemia Severity Triage in CBRNE Emergencies.Diagnostics (Basel, Switzerland) · 2024Article
- Attention-based Imputation of Missing Values in Electronic Health Records Tabular Data.Proceedings. IEEE International Conference on Healthcare Informatics · 2024Article
- Relative importance of triglyceride glucose index combined with body mass index in predicting recovery from prediabetic state to normal fasting glucose: a cohort analysis based on a Chinese physical examination population.Lipids in health and disease · 2024Article
- Comparison of the effects of imputation methods for missing data in predictive modelling of cohort study datasets.BMC medical research methodology · 2024Article
- Moving Beyond Medical Statistics: A Systematic Review on Missing Data Handling in Electronic Health Records.Health data science · 2024Review
- Enhanced feature selection and ensemble learning for cardiovascular disease prediction: hybrid GOL2-2 T and adaptive boosted decision fusion with babysitting refinement.Frontiers in medicine · 2024Article
- A Predictive Model of Major Postoperative Respiratory Adverse Events in Pediatric Patients Undergoing Rigid Bronchoscopy for Exploration and Foreign Body Removal.Journal of clinical medicine · 2023Article
- Application of wastewater-based surveillance and copula time-series model for COVID-19 forecasts.The Science of the total environment · 2023Article
- Deep imputation of missing values in time series health data: A review with benchmarking.Journal of biomedical informatics · 2023Review
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3 authors.
Funding
Abstract
Missing values in tabular data restrict the use and performance of machine learning, requiring the imputation of missing values. The most popular imputation algorithm is arguably multiple imputations using chains of equations (MICE), which estimates missing values from linear conditioning on observed values. This paper proposes methods to improve both the imputation accuracy of MICE and the classification accuracy of imputed data by replacing MICE's linear regressors with ensemble learning and deep neural networks (DNN). The imputation accuracy is further improved by characterizing individual samples with cluster labels (CISCL) obtained from the training data. Our extensive analyses involving six tabular data sets, up to 80% missing values, and three missing types (missing completely at random, missing at random, missing not at random) reveal that ensemble or deep learning within MICE is superior to the baseline MICE (b-MICE), both of which are consistently outperformed by CISCL. Results show that CISCL + b-MICE outperforms b-MICE for all percentages and types of missingness. Our proposed DNN-based MICE and gradient boosting MICE plus CISCL (GB-MICE-CISCL) outperform seven state-of-the-art imputation algorithms in most experimental cases. The classification accuracy of GB-MICE imputed data is further improved by our proposed GB-MICE-CISCL imputation method across all missingness percentages. Results also reveal a shortcoming of the MICE framework at high missingness (>50%) and when the missing type is not random. This paper provides a generalized approach to identifying the best imputation model for a data set with a missingness percentage and type.
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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.