Missing Value Imputation Methods Research Articles

Attention-based Imputation of Missing Values in Electronic Health Records Tabular Data.

The imputation of missing values (IMV) in electronic health records tabular data is crucial to enable machine learning for patient-specific predictive modeling. While IMV methods are developed in biostatistics and recently in machine learning, deep learning-based solutions have shown limited success in learning tabular data. This paper proposes a novel attention-based missing value imputation framework that learns to reconstruct data with missing values leveraging between-feature (self-attention) or between-sample attentions. We adopt data manipulation methods used in contrastive learning to improve the generalization of the trained imputation model. The proposed self-attention imputation method outperforms state-of-the-art statistical and machine learning-based (decision-tree) imputation methods, reducing the normalized root mean squared error by 18.4% to 74.7% on five tabular data sets and 52.6% to 82.6% on two electronic health records data sets. The proposed attention-based missing value imputation method shows superior performance across a wide range of missingness (10% to 50%) when the values are missing completely at random.

Alleviating cold start and sparsity problems in the micro, small, and medium enterprises marketplace using clustering and imputation techniques

Recommendation systems are often implemented in e-commerce and micro, small, and medium enterprises (MSMEs) marketplaces to improve consumer services by providing product recommendations according to their interests. However, it still faces problems, namely sparsity and cold start, thus affecting the quality of recommendations. This research proposes clustering and imputation techniques to overcome this problem. The clustering technique used is k-means, while the missing value imputation method uses average values. The imputation results are then implemented in the k-nearest neighbor (KNN) and naïve Bayes algorithms and evaluated based on performance accuracy. Experimental results show an increase in accuracy of 16.48% in the KNN algorithm from 83.52% to 100%. Meanwhile, the naïve Bayes algorithm increased accuracy by 35.30% from 64.70% to 100%.

OmicsMIC: a comprehensive benchmarking platform for robust comparison of imputation methods in mass spectrometry-based omics data.

Mass spectrometry is a powerful and widely used tool for generating proteomics, lipidomics and metabolomics profiles, which is pivotal for elucidating biological processes and identifying biomarkers. However, missing values in mass spectrometry-based omics data may pose a critical challenge for the comprehensive identification of biomarkers and elucidation of the biological processes underlying human complex disorders. To alleviate this issue, various imputation methods for mass spectrometry-based omics data have been developed. However, a comprehensive comparison of these imputation methods is still lacking, and researchers are frequently confronted with a multitude of options without a clear rationale for method selection. To address this pressing need, we developed omicsMIC (mass spectrometry-based omics with Missing values Imputation methods Comparison platform), an interactive platform that provides researchers with a versatile framework to evaluate the performance of 28 diverse imputation methods. omicsMIC offers a nuanced perspective, acknowledging the inherent heterogeneity in biological data and the unique attributes of each dataset. Our platform empowers researchers to make data-driven decisions in imputation method selection based on real-time visualizations of the outcomes associated with different imputation strategies. The comprehensive benchmarking and versatility of omicsMIC make it a valuable tool for the scientific community engaged in mass spectrometry-based omics research. omicsMIC is freely available at https://github.com/WQLin8/omicsMIC.

Deep learning based decision tree ensembles for incomplete medical datasets.

In practice, the collected datasets for data analysis are usually incomplete as some data contain missing attribute values. Many related works focus on constructing specific models to produce estimations to replace the missing values, to make the original incomplete datasets become complete. Another type of solution is to directly handle the incomplete datasets without missing value imputation, with decision trees being the major technique for this purpose. To introduce a novel approach, namely Deep Learning-based Decision Tree Ensembles (DLDTE), which borrows the bounding box and sliding window strategies used in deep learning techniques to divide an incomplete dataset into a number of subsets and learning from each subset by a decision tree, resulting in decision tree ensembles. Two medical domain problem datasets contain several hundred feature dimensions with the missing rates of 10% to 50% are used for performance comparison. The proposed DLDTE provides the highest rate of classification accuracy when compared with the baseline decision tree method, as well as two missing value imputation methods (mean and k-nearest neighbor), and the case deletion method. The results demonstrate the effectiveness of DLDTE for handling incomplete medical datasets with different missing rates.

Variational Bayesian Student's-t Mixture Model With Closed-Form Missing Value Imputation for Robust Process Monitoring of Low-Quality Data.

Due to record errors, transmission interruptions, etc., low-quality process data, including outliers and missing data, commonly exist in real industrial processes, challenging the accurate modeling and reliable monitoring of the operating statuses. In this study, a novel variational Bayesian Student's-t mixture model (VBSMM) with a closed-form missing value imputation method is proposed to develop a robust process monitoring scheme for low-quality data. First, a new paradigm for the variational inference of Student's-t mixture model is proposed to develop a robust VBSMM model, which optimizes the variational posteriors in an extended feasible region. Second, conditioned on the complete and partially missing data information, a closed-form missing value imputation method is derived to address the challenges of outliers and multimodality in accurate data recovery. Then, a robust online monitoring scheme that can maintain its fault detection performance in the presence of poor data quality is developed, where a novel monitoring statistic called the expected variational distance (EVD) is first proposed to quantify the changes in operating conditions and can be easily extended to other variational mixture models. Case studies on a numerical simulation and a real-world three-phase flow facility illustrate the superiority of the proposed method in missing value imputation and fault detection of low-quality data.

Missing value imputation on gene expression data using bee-based algorithm to improve classification performance.

Existing missing value imputation methods focused on imputing the data regarding actual values towards a completion of datasets as an input for machine learning tasks. This work proposes an imputation of missing values towards improvement of accuracy performance for classification. The proposed method was based on bee algorithm and the use of k-nearest neighborhood with linear regression to guide on finding the appropriate solution in prevention of randomness. Among the processes, GINI importance score was utilized in selecting values for imputation. The imputed values thus reflected on improving a discriminative power in classification tasks instead of replicating the actual values from the original dataset. In this study, we evaluated the proposed method against frequently used imputation methods such as k-nearest neighborhood, principal components analysis, nonlinear principal, and component analysis to compare root mean square error results and accuracy of using imputed datasets in a classification task. The experimental results indicated that our proposed method obtained the best accuracy results from all datasets comparing to other methods. In comparison to original dataset, the classification model from imputed datasets yielded 15-25% higher accuracy in class prediction. From analysis, the results showed that feature ranking used in a classification process was affected and lead to noticeably change in informativeness as the imputed data from the proposed method played the role to boost a discriminating power.

Machine-Learning-Based Imputation Method for Filling Missing Values in Ground Meteorological Observation Data

Ground meteorological observation data (GMOD) are the core of research on earth-related disciplines and an important reference for societal production and life. Unfortunately, due to operational issues or equipment failures, missing values may occur in GMOD. Hence, the imputation of missing data is a prevalent issue during the pre-processing of GMOD. Although a large number of machine-learning methods have been applied to the field of meteorological missing value imputation and have achieved good results, they are usually aimed at specific meteorological elements, and few studies discuss imputation when multiple elements are randomly missing in the dataset. This paper designed a machine-learning-based multidimensional meteorological data imputation framework (MMDIF), which can use the predictions of machine-learning methods to impute the GMOD with random missing values in multiple attributes, and tested the effectiveness of 20 machine-learning methods on imputing missing values within 124 meteorological stations across six different climatic regions based on the MMDIF. The results show that MMDIF-RF was the most effective missing value imputation method; it is better than other methods for imputing 11 types of hourly meteorological elements. Although this paper applied MMDIF to the imputation of missing values in meteorological data, the method can also provide guidance for dataset reconstruction in other industries.

Physics-informed machine learning with high-throughput design module for evaluating rupture life and guiding design of oxide/oxide ceramic matrix composites

In this paper, a unified physics-informed machine learning (PIML) model is proposed for the accurate prediction of the creep rupture life of oxide/oxide ceramic matrix composites (CMCs) by incorporating prior physical information and microstructure features. The collected dataset undergoes missing value imputation and standardization methods for processing. Key feature parameters that influence rupture life are identified through global sensitivity analysis. Furthermore, a high-throughput design module is developed to explore optimal composition under specific creep conditions, providing design guidelines for new materials in oxide/oxide CMCs. This study demonstrates that the predicted results consistently fall within the ±3 error bands. Improved life expectancy is attained with fiber modulus (220–290 GPa) and matrix modulus (>310 GPa). The transformation from expensive creep testing to low-cost exploration of modulus and proportional limit is achieved, offering theoretical support and analytical methods for the design of CMC components and the development of new materials for high-temperature applications.

ProJect: a powerful mixed-model missing value imputation method.

Missing values (MVs) can adversely impact data analysis and machine-learning model development. We propose a novel mixed-model method for missing value imputation (MVI). This method, ProJect (short for Protein inJection), is a powerful and meaningful improvement over existing MVI methods such as Bayesian principal component analysis (PCA), probabilistic PCA, local least squares and quantile regression imputation of left-censored data. We rigorously tested ProJect on various high-throughput data types, including genomics and mass spectrometry (MS)-based proteomics. Specifically, we utilized renal cancer (RC) data acquired using DIA-SWATH, ovarian cancer (OC) data acquired using DIA-MS, bladder (BladderBatch) and glioblastoma (GBM) microarray gene expression dataset. Our results demonstrate that ProJect consistently performs better than other referenced MVI methods. It achieves the lowest normalized root mean square error (on average, scoring 45.92% less error in RC_C, 27.37% in RC_full, 29.22% in OC, 23.65% in BladderBatch and 20.20% in GBM relative to the closest competing method) and the Procrustes sum of squared error (Procrustes SS) (exhibits 79.71% less error in RC_C, 38.36% in RC full, 18.13% in OC, 74.74% in BladderBatch and 30.79% in GBM compared to the next best method). ProJect also leads with the highest correlation coefficient among all types of MV combinations (0.64% higher in RC_C, 0.24% in RC full, 0.55% in OC, 0.39% in BladderBatch and 0.27% in GBM versus the second-best performing method). ProJect's key strength is its ability to handle different types of MVs commonly found in real-world data. Unlike most MVI methods that are designed to handle only one type of MV, ProJect employs a decision-making algorithm that first determines if an MV is missing at random or missing not at random. It then employs targeted imputation strategies for each MV type, resulting in more accurate and reliable imputation outcomes. An R implementation of ProJect is available at https://github.com/miaomiao6606/ProJect.

Missing Value Imputation Methods for Electronic Health Records

Electronic health records (EHR) are patient-level information, e.g., laboratory tests and questionnaires, stored in electronic format. Compared to physical records, the EHR alternative allows patients to access their data easily and helps staff with management procedural tasks such as information sharing across different organizations. Moreover, this type of data is commonly used by researchers for predictive and classification purposes, employing statistical and machine learning methods. However, missingness is a phenomenon that is observed very frequently for such measurements. Even though this missingness is often significant, it is usually treated poorly with either case deletion or simple methods, resulting in suboptimal and/or inaccurate predictive results. This happens because the simple methods, e.g., k-nearest neighbors (kNN) and mean/mode imputation, fail in most cases to incorporate the complex relationships that define these medical datasets. To address these limitations, in this paper we test and improve state-of-the-art missing data imputation models and practices. We propose a new missing value imputation method based on denoising autoencoders (DAE) with kNN for the pre-imputation task. We optimize the training methodology by re-applying kNN to the missing data every <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</i> epochs using a different value for the variable <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> each time to yield more accurate results. We also revise a state-of-the-art missing data imputation approach based on a generative adversarial network (GAN). Using this as a baseline, we introduce improvements regarding both the architecture and the training procedure. These models are compared with the ones usually employed within clinical research studies for both the task of imputation and post-imputation prediction. Results show that our proposed deep learning approaches outperform the standard baselines, yielding better imputation and predictive results.

An Improved Bi-LSTM-Based Missing Value Imputation Approach for Pregnancy Examination Data

In recent years, the development of computer technology has promoted the informatization and intelligentization of hospital management systems and thus produced a large amount of medical data. These medical data are valuable resources for research. We can obtain inducers and unknown symptoms that can help discover diseases and make earlier diagnoses. Hypertensive disorder in pregnancy (HDP) is a common obstetric complication in pregnant women, which has severe adverse effects on the life safety of pregnant women and fetuses. However, the early and mid-term symptoms of HDP are not obvious, and there is no effective solution for it except for terminating the pregnancy. Therefore, detecting and preventing HDP is of great importance. This study aims at the preprocessing of pregnancy examination data, which serves as a part of HDP prediction. We found that the problem of missing data has a large impact on HDP prediction. Unlike general data, pregnancy examination data have high dimension and a high missing rate, are in a time series, and often have many non-linear relations. Current methods are not able to process the data effectively. To this end, we propose an improved bi-LSTM-based missing value imputation approach. It combines traditional machine learning and bidirectional LSTM to deal with missing data of pregnancy examination data. Our missing value imputation method obtains a good effect and improves the accuracy of the later prediction of HDP using examination data.

Prediction of new onset atrial fibrillation recurrence or persistence with artificial intelligence: first insights of the PRAFAI study

Abstract Introduction Atrial fibrillation (AF) is the most prevalent arrhythmia in the world and it is associated with a high rate of cardiovascular morbidity and mortality. A higher burden of atrial fibrillation is related to more adverse cardiovascular events and the rhythm control strategy (maintenance or recovery of sinus rhythm) is particularly indicated in patients with paroxysmal symptomatic atrial fibrillation. All things considered, in order to select the optimal therapeutic strategy, it might be useful to accurately predict the probability of recurrence or permanence of AF after its onset. The predictive models implemented so far, show a limited predictive power or were built on samples of patients undergoing specific therapies. Thus, developing an specific model based on features derived from electronic health records (EHR) of patients with a first AF episode seems essential. Purpose To develop an artificial intelligence powered predictive model based on EHR variables in order to predict the AF recurrence after its onset. Methods The methodological design of the research is based on the analysis of secondary data from a retrospective cohort obtained from the EHR of a tertiary hospital. All new onset AF from 2015 to 2018 were identified by ICD codes, and a 2 years EHR driven follow-up was made to determine the AF recurrence or permanence of the arrhythmia in those patients. A systematic review was pursued to determine which features exhibited a greater predictive power for AF recurrence, and they were inquired to the system for the mentioned participants. After the standardization of the variables and a feature selection process, several missing value imputation methods and around 12 different Machine Learning algorithms were tested to predict the AF recurrence in a subset of patients who developed AF in the first semester of 2015, in order to select the algorithms with the highest prediction accuracy for further tests. Results 523 patients had an AF onset in the first semester of 2015. Among them, 310 (59.3%) had AF recurrence in a 2 years period. A support vector machine (SVM) algorithm with a median imputation method to handle missing values performed the best and had an 0.77 AUC in the training set and 0.71 in the test set. Hyperthyroidism, vasodilator drug use and obstructive sleep apnea were notoriously related to AF recurrence, while Ic antiarrhythmic drugs, AF ablation, cognitive impairment and anxiety were the most influential variables for the absence of recurrence. Classical AF recurrence factors such as age or left atrium diameter ratify their role in this setting. Conclusion A SVM Machine Learning algorithm discloses an accurate AF recurrence probability based on EHR features. Pharmacologic and epidemiological variables, added to classically assessed ones, seem to be specially related to this arrhythmic recurrence. Further research including the whole cohort features will probably improve the accuracy of this selected SVM algorithm. Funding Acknowledgement Type of funding sources: None.

Dealing with missing values in proteomics data.

Proteomics data are often plagued with missingness issues. These missing values (MVs) threaten the integrity of subsequent statistical analyses by reduction of statistical power, introduction of bias, and failure to represent the true sample. Over the years, several categories of missing value imputation (MVI) methods have been developed and adapted for proteomics data. These MVI methods perform their tasks based on different prior assumptions (e.g., data is normally or independently distributed) and operating principles (e.g., the algorithm is built to address random missingness only), resulting in varying levels of performance even when dealing with the same dataset. Thus, to achieve a satisfactory outcome, a suitable MVI method must be selected. To guide decision making on suitable MVI method, we provide a decision chart which facilitates strategic considerations on datasets presenting different characteristics. We also bring attention to other issues that can impact proper MVI such as the presence of confounders (e.g., batch effects) which can influence MVI performance. Thus, these too, should be considered during or before MVI.

Prediction of new onset atrial fibrillation recurrence or persistence with artificial intelligence: first insights of the PRAFAI study

Abstract Introduction Atrial fibrillation (AF) is the most prevalent arrhythmia in the world and it is associated with a high rate of cardiovascular morbidity and mortality. A higher burden of atrial fibrillation is related to more adverse cardiovascular events and the rhythm control strategy (maintenance or recovery of sinus rhythm) is particularly indicated in patients with paroxysmal symptomatic atrial fibrillation. All things considered, in order to select the optimal therapeutic strategy, it might be useful to accurately predict the probability of recurrence or permanence of AF after its onset. The predictive models implemented so far, show a limited predictive power or were built on samples of patients undergoing specific therapies. Thus, developing an specific model based on features derived from electronic health records (EHR) of patients with a first AF episode seems essential. Purpose To develop an artificial intelligence powered predictive model based on EHR variables in order to predict the AF recurrence after its onset. Methods The methodological design of the research is based on the analysis of secondary data from a retrospective cohort obtained from the EHR of a tertiary hospital. All new onset AF from 2015 to 2018 were identified by ICD codes, and a 2 years EHR driven follow-up was made to determine the AF recurrence or permanence of the arrhythmia in those patients. A systematic review was pursued to determine which features exhibited a greater predictive power for AF recurrence, and they were inquired to the system for the mentioned participants. After the standardization of the variables and a feature selection process, several missing value imputation methods and around 12 different Machine Learning algorithms were tested to predict the AF recurrence in a subset of patients who developed AF in the first semester of 2015, in order to select the algorithms with the highest prediction accuracy for further tests. Results 523 patients had an AF onset in the first semester of 2015. Among them, 310 (59.3%) had AF recurrence in a 2 years period. A support vector machine (SVM) algorithm with a median imputation method to handle missing values performed the best and had an 0.77 AUC in the training set and 0.71 in the test set. Hyperthyroidism, vasodilator drug use and obstructive sleep apnea were notoriously related to AF recurrence, while Ic antiarrhythmic drugs, AF ablation, cognitive impairment and anxiety were the most influential variables for the absence of recurrence. Classical AF recurrence factors such as age or left atrium diameter ratify their role in this setting. Conclusion A SVM Machine Learning algorithm discloses an accurate AF recurrence probability based on EHR features. Pharmacologic and epidemiological variables, added to classically assessed ones, seem to be specially related to this arrhythmic recurrence. Further research including the whole cohort features will probably improve the accuracy of this selected SVM algorithm. Funding Acknowledgement Type of funding sources: None.

Influence of vehicular emissions on the levels of polycyclic aromatic hydrocarbons (PAHs) in urban and industrial areas of La Plata, Argentina.

Polycyclic aromatic hydrocarbons (PAHs) are considered potentially toxic, even carcinogenic, because of their affection to public health and the environment. It is necessary to know their ambient levels and the origin of these pollutants in order to mitigate them. A concerning scenario is the one in which commercial/administrative, industrial, and residential activities coexist. In this context, Gran La Plata (Argentina) presents such characteristics, in addition to the presence of one of the most important petrochemical complexes in the country and intense vehicular traffic. The source apportionment of PAH emission in the region, associated to 10-µm and 2.5-µm particulate matter fractions, was studied. First, different missing value imputation methods were evaluated for PAH databases. GSimp presented a better performance, with mean concentrations of ∑PAHs of 65.8 ± 40.2ngm-3 in PM10 and 39.5 ± 18.0ngm-3 in PM2.5. For both fractions, it was found that the highest contribution was associated with low molecular weight PAHs (3 rings), with higher concentrations of anthracene. Emission sources were identified by using principal component analysis (PCA) together with multiple linear regression (MLR) and diagnostic ratios of PAHs. The results showed that the main emission source is associated with vehicular traffic in both fractions. Classification by discriminant analysis showed that emissions can be identified by region and that fluoranthene, benzo(a)anthracene, and anthracene in PM10 and anthracene and phenanthrene in PM2.5 are a characteristic of emissions from the petrochemical complex.

Improved GSimp: A Flexible Missing Value Imputation Method to Support Regulatory Bioequivalence Assessment.

Missing values are not uncommon in in vivo bioequivalence (BE) studies and pose non-trivial challenges for BE assessment. Missing values typically appear as a mixture of different types, such as Missing Not at Random (MNAR) and Missing Completely at Random (MCAR), however, current data imputation methods were usually developed for a certain type of missing values (e.g., MNAR). Among them, an iterative Gibbs sampler-based left-censored missing value imputation approach (GSimp) was recently developed and showed superior performance over other methods in handling MNAR data. In this study, we introduce an improved GSimp ("Improved GSimp" thereafter) that offers flexibility in handling mixed types of missing data and better imputation accuracy to support BE assessment for studies with missing values. Simulations mimicking different missing value scenarios (e.g., mixture of different missing types and proportion of missing values) were conducted to compare performance of the Improved GSimp with other methods (e.g., original GSimp and half of minimal value). Normalized root mean square error (NRMSE) was used to evaluate imputation accuracy. Our results showed that the Improved GSimp always had the best accuracy in all simulated scenarios compared to other methods.

A comparison of missing values imputation methods applied to precipitation of two semi-arid and humid regions of México

Climatological data with unreliable or missing values is an important area of research, and multiple methods are available to fill in missing data and evaluate data quality. Our study aims to compare the performance of different methods for estimating missing values that are explicitly designed for precipitation and multipurpose hydrological data. The climate variable used for the analysis was daily precipitation. We considered two different climate and orographic regions to evaluate the effects of altitude, precipitation regime and percentage of missing data on the Mean Absolute Error of imputed values and using a homogeneity evaluation of meteorological stations. We excluded from the analysis meteorological stations with more than 25% missing data. In the semi-arid region, ReddPrec (optimal for 9 stations), and GCIDW (optimal for 8) were the best performing methods for the 23 stations, with average MAE values of 1.63 mm/day and 1.46 mm/day, respectively. In the humid region, GCIDW was optimal in ~59% of stations, EM in ~24%, and ReddPrec in ~17%, with average MAE values of ~6.0 mm/day, 6.5 mm/day and ~9.8 mm/day, respectively. This research makes an important contribution to identifying the most appropriate methods to impute daily precipitation in different climatic regions of Mexico based on efficiency indicators and homogeneity evaluation.

Discrete Missing Data Imputation Using Multilayer Perceptron and Momentum Gradient Descent.

Data are a strategic resource for industrial production, and an efficient data-mining process will increase productivity. However, there exist many missing values in data collected in real life due to various problems. Because the missing data may reduce productivity, missing value imputation is an important research topic in data mining. At present, most studies mainly focus on imputation methods for continuous missing data, while a few concentrate on discrete missing data. In this paper, a discrete missing value imputation method based on a multilayer perceptron (MLP) is proposed, which employs a momentum gradient descent algorithm, and some prefilling strategies are utilized to improve the convergence speed of the MLP. To verify the effectiveness of the method, experiments are conducted to compare the classification accuracy with eight common imputation methods, such as the mode, random, hot-deck, KNN, autoencoder, and MLP, under different missing mechanisms and missing proportions. Experimental results verify that the improved MLP model (IMLP) can effectively impute discrete missing values in most situations under three missing patterns.

Optimization of Imputation Strategies for High-Resolution Gas Chromatography-Mass Spectrometry (HR GC-MS) Metabolomics Data.

Gas chromatography–coupled mass spectrometry (GC–MS) has been used in biomedical research to analyze volatile, non-polar, and polar metabolites in a wide array of sample types. Despite advances in technology, missing values are still common in metabolomics datasets and must be properly handled. We evaluated the performance of ten commonly used missing value imputation methods with metabolites analyzed on an HR GC–MS instrument. By introducing missing values into the complete (i.e., data without any missing values) National Institute of Standards and Technology (NIST) plasma dataset, we demonstrate that random forest (RF), glmnet ridge regression (GRR), and Bayesian principal component analysis (BPCA) shared the lowest root mean squared error (RMSE) in technical replicate data. Further examination of these three methods in data from baboon plasma and liver samples demonstrated they all maintained high accuracy. Overall, our analysis suggests that any of the three imputation methods can be applied effectively to untargeted metabolomics datasets with high accuracy. However, it is important to note that imputation will alter the correlation structure of the dataset and bias downstream regression coefficients and p-values.

Adaptive multiple imputations of missing values using the class center

Big data has become a core technology to provide innovative solutions in many fields. However, the collected dataset for data analysis in various domains will contain missing values. Missing value imputation is the primary method for resolving problems involving incomplete datasets. Missing attribute values are replaced with values from a selected set of observed data using statistical or machine learning methods. Although machine learning techniques can generate reasonably accurate imputation results, they typically require longer imputation durations than statistical techniques. This study proposes the adaptive multiple imputations of missing values using the class center (AMICC) approach to produce effective imputation results efficiently. AMICC is based on the class center and defines a threshold from the weighted distances between the center and other observed data for the imputation step. Additionally, the distance can be an adaptive nearest neighborhood or the center to estimate the missing values. The experimental results are based on numerical, categorical, and mixed datasets from the University of California Irvine (UCI) Machine Learning Repository with introduced missing values rate from 10 to 50% in 27 datasets. The proposed AMICC approach outperforms the other missing value imputation methods with higher average accuracy at 81.48% which is higher than those of other methods about 9 – 14%. Furthermore, execution time is different from the Mean/Mode method, about seven seconds; moreover, it requires significantly less time for imputation than some machine learning approaches about 10 – 14 s.