Missing Value Imputation Methods Research Articles

Gated Recurrent Unit Coupled with Projection to Model Plane Imputation for the PM2.5 Prediction for Guangzhou City, China

Air pollution is generating serious health issues as well as threats to our natural ecosystem. Accurate prediction of PM2.5 can help taking preventive measures for reducing air pollution. The periodic pattern of PM2.5 can be modeled with recurrent neural networks to predict air quality. To the best of the author’s knowledge, very limited work has been conducted on the coupling of missing value imputation methods with gated recurrent unit (GRU) for the prediction of PM2.5 concentration of Guangzhou City, China. This paper proposes the combination of project to model plane (PMP) with GRU for the superior prediction performance of PM2.5 concentration of Guangzhou City, China. Initially, outperforming the missing value imputation method PMP is proposed for air quality data under consideration by making a comparison study on various methods such as KDR, TSR, IA, NIPALS, DA, and PMP. Secondly, it presents GRU in combination with PMP to show its superiority on other machine learning techniques such as LSSVM and two other RNN variants, LSTM and Bi-LSTM. For this study, data for Guangzhou City were collected from China’s governmental air quality website. Data contained daily values of PM2.5, PM10, O3, SOx, NOx, and CO. This study has employed RMSE, MAPE, and MEDAE as model prediction performance criteria. Comparison of prediction performance criteria on the test data showed GRU in combination with PMP has outperformed the LSSVM and other RNN variants LSTM and Bi-LSTM for Guangzhou City, China. In comparison with prediction performance of LSSVM, GRU improved the prediction performance on test data by 40.9% RMSE, 48.5% MAPE, and 50.4% MEDAE.

Handling Massive Proportion of Missing Labels in Multivariate Long-Term Time Series Forecasting

Training Deep Learning (DL) models with missing labels is a challenge in diverse engineering applications. Missing value imputation methods have been proposed to try to address this problem, but their performance is affected with Massive Proportion of Missing Labels (MPML). This paper presents a approach for handling MPML in Multivariate Long-Term Time Series Forecasting. It is an two-step process where interpolation (using Gaussian Processes Regression (GPR) and domain knowledge from experts) and prediction model are separated to enable the integration of prior domain knowledge. First, a set of samples of the possible interpolation of the missing outputs are generated by the GPR based on the domain knowledge. Second, the observed input sensor data and interpolated labels from GPR are used to train the prediction model. We evaluated our approach with the development of a soft-sensor with one real datasets to forecast the biomass during recombinant adeno-associated virus (rAAV) production in bioreactors. Our experimental results demonstrate the potential of the approach through quantitative evaluation of the generated forecasts in a case that would be extremely difficult to train a DL model due to MPML.

RMisbeta: A robust missing value imputation approach in transcriptomics and metabolomics data

Transcriptomics and metabolomics data often contain missing values or outliers due to limitations of the data acquisition techniques. Most of the statistical methods require complete datasets for downstream analysis. A number of methods have been developed for missing value imputation using the classical mean and variance based on maximum likelihood estimators, which are not robust against outliers. Consequently, the performance of these methods deteriorates in the presence of outliers. Hence precise imputation of missing values and outliers handling are both concurrently important. Therefore, in this paper, we developed a robust iterative approach using robust estimators based on the minimum beta divergence method, which simultaneously impute missing values and outliers. We investigate the performance of the proposed method in a comparison with six frequently used missing value imputation methods such as Zero, KNN, robust SVD, EM, random forest (RF) and weighted least square approach (WLSA) through feature selection using both simulated and real datasets. Ten performance indices were used to explore the optimal method such as Frobenius norm (FOBN), accuracy (ACC), sensitivity (SN), specificity (SP), positive predictive value (PPV), negative predictive value (NPV), detection rate (DR), misclassification error rate (MER), the area under the ROC curve (AUC) and computational runtime. Evaluation based on both simulated and real data suggests the superiority of the proposed method over the other traditional methods in terms of various rates of outliers and missing values. The suggested approach also keeps almost equal performance in absence of outliers with the other methods. The proposed method is accurate, simple, and consumes lower computational time compared to the other methods. Therefore, our recommendation is to apply the proposed procedure for large-scale transcriptomics and metabolomics data analysis. The computational tool has been implemented in an R package, which is publicly available from https://CRAN.R-project.org/package=rMisbeta.

Left-Censored Missing Value Imputation Approach for MS-Based Proteomics Data with GSimp.

Missing values caused by the limit of detection or quantification (LOD/LOQ) were widely observed in mass spectrometry (MS)-based omics studies and could be recognized as missing not at random (MNAR). MNAR leads to biased statistical estimations and jeopardizes downstream analyses. Although a wide range of missing value imputation methods was developed for omics studies, a limited number of methods were designed appropriately for the situation of MNAR. To facilitate MS-based omics studies, we introduce GSimp, a Gibbs sampler-based missing value imputation approach, to deal with left-censor missing values in MS-proteomics datasets. In this book, we explain the MNAR and elucidate the usage of GSimp for MNAR in detail.

Diagnosis of Cardiovascular Diseases using Artificial Intelligence Techniques: A Review

In the last couple of decades, many techniques have been introduced for medical support system. One alarming field in medical health care is cardiovascular disease as millions of deaths occur every year because of this. Thus, diagnosis of heart disease has always been one of the most important issues. For predicting and diagnosis of cardiovascular disease, skilled and experienced physicians are needed. As this is an era of technology, researchers have been proposed many algorithms and learning techniques for assisting the physicians. The aim of this research work is to thoroughly analyze these algorithms and methods. This article has explored the used datasets, feature selection techniques and missing value imputation methods, and finally compared their performances.

Training load monitoring in team sports: a practical approach to addressing missing data

ABSTRACT Training load (TL) is a modifiable risk factor that may provide practitioners with opportunities to mitigate injury risk and increase sports performance. A regular problem encountered by practitioners, however, is the issue of missing TL data. The purpose of this study was to examine the impact of missing TL data in team sports and to offer a practical and effective method of missing value imputation (MVI) to address this. Session rating of perceived exertion (sRPE) data from 10 male professional soccer players (age, 24.8 ± 5.0 years; height, 181.2 ± 5.1 cm; mass, 78.7 ± 6.4 kg) were collected over a 32-week season. Data were randomly removed at a range of 5–50% in increments of 5% and data were imputed using 12 MVI methods. Performance was measured using the normalized root-mean-square error and mean of absolute deviations. The best-fitting MVI method across all levels of missingness was Daily Team Mean (DTMean). Not addressing missing sRPE data may lead to more inaccurate calculations of other TL metrics (e.g., acute chronic workload ratio, training monotony, training strain). The DTMean MVI method may provide practitioners with a practical and effective approach to addressing the negative consequences of missing TL data.

A systematic review of machine learning-based missing value imputation techniques

PurposeThe primary aim of this study is to review the studies from different dimensions including type of methods, experimentation setup and evaluation metrics used in the novel approaches proposed for data imputation, particularly in the machine learning (ML) area. This ultimately provides an understanding about how well the proposed framework is evaluated and what type and ratio of missingness are addressed in the proposals. The review questions in this study are (1) what are the ML-based imputation methods studied and proposed during 2010–2020? (2) How the experimentation setup, characteristics of data sets and missingness are employed in these studies? (3) What metrics were used for the evaluation of imputation method?Design/methodology/approachThe review process went through the standard identification, screening and selection process. The initial search on electronic databases for missing value imputation (MVI) based on ML algorithms returned a large number of papers totaling at 2,883. Most of the papers at this stage were not exactly an MVI technique relevant to this study. The literature reviews are first scanned in the title for relevancy, and 306 literature reviews were identified as appropriate. Upon reviewing the abstract text, 151 literature reviews that are not eligible for this study are dropped. This resulted in 155 research papers suitable for full-text review. From this, 117 papers are used in assessment of the review questions.FindingsThis study shows that clustering- and instance-based algorithms are the most proposed MVI methods. Percentage of correct prediction (PCP) and root mean square error (RMSE) are most used evaluation metrics in these studies. For experimentation, majority of the studies sourced the data sets from publicly available data set repositories. A common approach is that the complete data set is set as baseline to evaluate the effectiveness of imputation on the test data sets with artificially induced missingness. The data set size and missingness ratio varied across the experimentations, while missing datatype and mechanism are pertaining to the capability of imputation. Computational expense is a concern, and experimentation using large data sets appears to be a challenge.Originality/valueIt is understood from the review that there is no single universal solution to missing data problem. Variants of ML approaches work well with the missingness based on the characteristics of the data set. Most of the methods reviewed lack generalization with regard to applicability. Another concern related to applicability is the complexity of the formulation and implementation of the algorithm. Imputations based on k-nearest neighbors (kNN) and clustering algorithms which are simple and easy to implement make it popular across various domains.

The Impact of COVID-19 on Students' Marks: A Bayesian Hierarchical Modeling Approach.

Due to COVID-19, universities across Canada were forced to undergo a transition from classroom-based face-to-face learning and invigilated assessments to online-based learning and non-invigilated assessments. This study attempts to empirically measure the impact of COVID-19 on students’ marks from eleven science, technology, engineering, and mathematics (STEM) courses using a Bayesian linear mixed effects model fitted to longitudinal data. The Bayesian linear mixed effects model is designed for this application which allows student-specific error variances to vary. The novel Bayesian missing value imputation method is flexible which seamlessly generates missing values given complete data. We observed an increase in overall average marks for the courses requiring lower-level cognitive skills according to Bloom’s Taxonomy and a decrease in marks for the courses requiring higher-level cognitive skills, where larger changes in marks were observed for the underachieving students. About half of the disengaged students who did not participate in any course assessments after the transition to online delivery were in special support.

An Exploration of Online Missing Value Imputation in Non-stationary Data Stream

Missing value imputation (MVI) is an important data preprocessing technique. In previous decades, MVI technique has been widely studied as well as most MVI approaches have been proposed by means of either statistics or machine learning techniques. However, all previous methods only focus on the static data, but ignore the imputation for the dynamic online data. It is intuitionistic that the imputation errors may be significantly increased when there exists concept drifts in the data stream. In this paper, we investigate the impact of adopting the conventional MVI methods in non-stationary data stream. Meanwhile, two slide time window-based strategies are proposed to alleviate this impact, where one is the plain average strategy, and the other is the logarithmic weighted average strategy that gradually adds the weights of instances along the time axis. Combining with the proposed strategies, three popular MVI techniques, mean imputation (MI), KNN imputation (KNNI) and the Bayesian principal component analysis imputation (BPCAI) are adopted, to indicate the effect of the strategies are irrelevant to the specific MVI technique. The experimental results on three different types' concept drift synthetic data sets and two real-world drifting data sets have presented the effectiveness and feasibility of the proposed strategies. Moreover, the impact of time window size has also been investigated for guiding the parameter settings in future practical applications.

A comparative study of evaluating missing value imputation methods in label-free proteomics

The presence of missing values (MVs) in label-free quantitative proteomics greatly reduces the completeness of data. Imputation has been widely utilized to handle MVs, and selection of the proper method is critical for the accuracy and reliability of imputation. Here we present a comparative study that evaluates the performance of seven popular imputation methods with a large-scale benchmark dataset and an immune cell dataset. Simulated MVs were incorporated into the complete part of each dataset with different combinations of MV rates and missing not at random (MNAR) rates. Normalized root mean square error (NRMSE) was applied to evaluate the accuracy of protein abundances and intergroup protein ratios after imputation. Detection of true positives (TPs) and false altered-protein discovery rate (FADR) between groups were also compared using the benchmark dataset. Furthermore, the accuracy of handling real MVs was assessed by comparing enriched pathways and signature genes of cell activation after imputing the immune cell dataset. We observed that the accuracy of imputation is primarily affected by the MNAR rate rather than the MV rate, and downstream analysis can be largely impacted by the selection of imputation methods. A random forest-based imputation method consistently outperformed other popular methods by achieving the lowest NRMSE, high amount of TPs with the average FADR < 5%, and the best detection of relevant pathways and signature genes, highlighting it as the most suitable method for label-free proteomics.

Variation in model performance by data cleanliness and classification methods in the prediction of 30-day ICU mortality, a US nationwide retrospective cohort and simulation study

ObjectiveThere has been a proliferation of approaches to statistical methods and missing data imputation as electronic health records become more plentiful; however, the relative performance on real-world problems is unclear.Materials...

Missing value imputation in multivariate time series with end-to-end generative adversarial networks

Missing values are inherent in multivariate time series because of multiple reasons, such as collection errors, which deteriorate the performance of follow-up analytic applications on the multivariate time series. Numerous missing value imputation methods have been proposed to mitigate the influence of missing values on multivariate time series analysis. Recently, inspired by the success of generative adversarial networks (GANs) in image generation, the GAN-2-Stage has been used to address the imputation problem with the generative model. Specifically, GAN-2-Stage employs GANs to impute the missing values. However, an extra phase is required to optimize the input random “noise” of the generator. In addition, the imputed values can be very different from real values because of the difficulty in training a GAN and the unstable generation process. Therefore, this paper proposes an end-to-end model to impute the missing values in a multivariate time series. Specifically, we introduce an encoder network into the standard GAN architecture that eliminates the input optimization phase in the GAN-2-Stage. Our generator utilizes real data during training to force the imputed values to be close to the real ones. Experiments on three real-world multivariate time series datasets demonstrate that the proposed model outperforms state-of-the-art methods in imputation tasks and downstream applications, including classification and regression.

Incomplete data ensemble classification using imputation-revision framework with local spatial neighborhood information

Most existing machine learning techniques require complete data. However, incomplete patterns are common in many real-world scenarios due to the missing values (MVs). Various Missing value imputation (MVI) methods have been proposed to recover the MVs. Each of them has its own advantages in some scenarios. However, on the one hand, few of them consider taking advantages of different MVI methods; on the other hand, how to improve the imputation performance with local information is still an open problem. This paper proposes an imputation-revision framework with local spatial neighborhood information for incomplete data classification. The proposed method endeavors to combine the advantages of several imputation methods. It first obtains several complete datasets which are pre-filled by various MVI methods. Then, it detects the local neighborhood information (LNI) of samples and revises MVs based on the LNI. Finally, ensemble technique is employed to give a final decision. Numerical experiments have verified the superiority of the proposed method in terms of both prediction accuracy and algorithm stability.

Sequel movie revenue prediction model based on sentiment analysis

PurposeSequel movies are very popular; however, there are limited studies on sequel movie revenue prediction. The purpose of this paper is to propose a sentiment analysis based model for sequel movie revenue prediction and to propose a missing value imputation method for the sequel revenue prediction dataset.Design/methodology/approachA sequel of a successful movie will most likely also be successful. Therefore, we propose a supervised learning approach in which data are created from sequel movies to predict the box-office revenue of an upcoming sequel. The algorithms used in the prediction are multiple linear regression, support vector machine and multilayer perceptron neural network.FindingsThe results show that using four sequel movies in a franchise to predict the box-office revenue of a fifth sequel achieved better prediction than using three sequels, which was also better than using two sequel movies.Research limitations/implicationsThe model produced will be beneficial to movie producers and other stakeholders in the movie industry in deciding the viability of producing a movie sequel.Originality/valuePrevious studies do not give priority to sequel movies in movie revenue prediction. Additionally, a new missing value imputation method was introduced. Finally, sequel movie revenue prediction dataset was prepared.

OP0010 EFFECTIVENESS OF THE MAKING IT WORK™ PROGRAM AT IMPROVING PRESENTEEISM AND WORK CESSATION IN WORKERS WITH INFLAMMATORY ARTHRITIS – RESULTS OF A RANDOMIZED CONTROLLED TRIAL

Background:Arthritis often leads to presenteeism (decreased at-work productivity), missed days from work and permanent work disability, leading to reduced quality of life and high costs to individuals and society. Yet, health services addressing the employment needs of people with arthritis are lacking.Objectives:We evaluated the effectiveness of the Making-it-WorkTM(MiW) program, an online self-management program developed to help people with inflammatory arthritis (IA) deal with employment issues.Methods:A multi-center RCT evaluated the effectiveness of MiW at improving presenteeism and preventing work cessation (WC) over two years. Participants were recruited from rheumatologist practices, consumer organizations and arthritis programs, in three Canadian provinces. Eligibility criteria: diagnosis of IA, employed, age 18-59, and concerned about ability to work. Participants were randomized 1:1 to MiW or usual care plus printed material on workplace tips. MiW consists of five online self-learning modules and group meetings, and individual vocational counselling and ergonomic consultations. Questionnaires were administered every 6 months. Outcomes were presenteeism [Rheumatoid Arthritis Work Instability Scale (RA-WIS)], time to WC ≥ 6 months, and time to WC ≥ 2 months (secondary outcome). Baseline characteristics (age, gender, ethnicity, occupation, education, disease duration and self-employment) were collected. Intention-to-treat (ITT) longitudinal analysis of RA-WIS using linear mixed effect regression models with 2-year comparison as primary endpoint and survival analysis for time to WC using Kaplan-Meier and Cox Proportional Hazard models were performed. Robustness analyses were conducted by using various missing values imputation methods like last observation carried forward, imputation using worse possible outcomes and model-based multiple imputations; using square root transformation of RA-WIS outcome; and adjusting for baseline covariates. SAS version 9.4 was used.Results:A total of 564 participants were recruited, with 478 (84.75%) completing 2-year follow-up. Baseline characteristics were similar between groups. Mean RA-WIS scores were significantly lower in the intervention group from 6 months onwards, with the greatest difference observed at 2 years (-1.78, 95%CI: -2.7, -0.9, p &lt; .0001), yielding a standardized effect size of 32%. Satisfactory robustness was observed. Work cessation occurred less often in intervention than control groups, but only reached statistical significance for WC ≥ 2 months (WC ≥ 6 months: 31 versus 44 events, aHR 0.70, 95%CI: 0.44, 1.11, p = 0.13; WC ≥ 2 months: 39 versus 61 events, aHR: 0.65, 95%CI: 0.43, 0.98, p = 0.04).Conclusion:Results of the RCT reveal the program was effective at improving presenteeism and preventing short-term WC. Effectiveness at preventing long-term work disability will be assessed at 5 years. This program fills one of the most important and costly unmet needs for people with inflammatory arthritis.

Evaluating missing value imputation methods for food composition databases

Missing data are a common problem in most research fields and introduce an element of ambiguity into data analysis. They can arise due to different reasons: mishandling of samples, measurement error, deleted aberrant value or simply lack of analysis. The nutrition domain is no exception to the problem of missing data. This paper addresses the problem of missing data in food composition databases (FCDBs). Missing data in FCDBs results in incomplete FCDBs, which have limited usage, because any dietary assessment can be performed only on a complete dataset. Most often, this problem is resolved by calculating means/medians from excising data in the same database or borrowing data from other FCDBs. These solutions introduce significant error. We focus on missing data imputation techniques based on methods for substituting missing values with statistical prediction: Non-Negative Matrix Factorization (NMF), Multiple Imputations by Chained Equations (MICE), Nonparametric Missing Value Imputation using Random Forest (MissForest), and K-Nearest Neighbors (KNN), and compared them with commonly used approaches - fill-in with mean, fill-in with median. The data used was from national FCDBs collected by EuroFIR (European Food Information Resource Network). The results show that the state-of-the-art methods for imputation yield better results than the traditional approaches.

ESTIMATION OF MISSING VALUES USING OPTIMISED HYBRID FUZZY C-MEANS AND MAJORITY VOTE FOR MICROARRAY DATA

Missing values are a huge constraint in microarray technologies towards improving and identifying disease-causing genes. Estimating missing values is an undeniable scenario faced by field experts. The imputation method is an effective way to impute the proper values to proceed with the next process in microarray technology. Missing value imputation methods may increase the classification accuracy. Although these methods might predict the values, classification accuracy rates prove the ability of the methods to identify the missing values in gene expression data. In this study, a novel method, Optimised Hybrid of Fuzzy C-Means and Majority Vote (opt-FCMMV), was proposed to identify the missing values in the data. Using the Majority Vote (MV) and optimisation through Particle Swarm Optimisation (PSO), this study predicted missing values in the data to form more informative and solid data. In order to verify the effectiveness of opt-FCMMV, several experiments were carried out on two publicly available microarray datasets (i.e. Ovary and Lung Cancer) under three missing value mechanisms with five different percentage values in the biomedical domain using Support Vector Machine (SVM) classifier. The experimental results showed that the proposed method functioned efficiently by showcasing the highest accuracy rate as compared to the one without imputations, with imputation by Fuzzy C-Means (FCM), and imputation by Fuzzy C-Means with Majority Vote (FCMMV). For example, the accuracy rates for Ovary Cancer data with 5% missing values were 64.0% for no imputation, 81.8% (FCM), 90.0% (FCMMV), and 93.7% (opt-FCMMV). Such an outcome indicates that the opt-FCMMV may also be applied in different domains in order to prepare the dataset for various data mining tasks.

Improved k-nearest neighbors approach for incomplete and contaminated gene expression datasets

With the rapid development of high-throughput DNA microarray technologies, researchers can measure expression profiles of thousands of genes simultaneously with low costs. These massive amounts of gene expression (GE) data often contain missing values or outliers due to various reasons of data generating process. Most of the statistical methods were developed based on complete dataset. As a result, for subsequent analysis using incomplete dataset, these methods strongly suffer and we cannot find our target. A numerous methods have been developed to impute missing values and they are available in the literature. Albeit, missing values imputation and outliers handling both are equally important for analyzing GE, most of the methods perform these tasks separately and produce misleading results. Therefore, in this paper, an attempt is made to develop a new hybrid approach which is robust against outliers and missing values, simultaneously. We demonstrate the performance of the proposed method in a comparison of popular missing value imputation method K-NN while performing feature selection using both simulated and real GE datasets. The Results obtain from simulated as well as real data studies show that the proposed method outperforms K-NN in presence of different percentages of missing values and outliers. On the other hand, in absence of outliers with missing values, the proposed method keeps equal performance with the other methods.&#x0D; J. bio-sci. 27: 31-41, 2019

Advances in Current Diabetes Proteomics: From the Perspectives of Label- free Quantification and Biomarker Selection

Due to its prevalence and negative impacts on both the economy and society, the diabetes mellitus (DM) has emerged as a worldwide concern. In light of this, the label-free quantification (LFQ) proteomics and diabetic marker selection methods have been applied to elucidate the underlying mechanisms associated with insulin resistance, explore novel protein biomarkers, and discover innovative therapeutic protein targets. The purpose of this manuscript is to review and analyze the recent computational advances and development of label-free quantification and diabetic marker selection in diabetes proteomics. Web of Science database, PubMed database and Google Scholar were utilized for searching label-free quantification, computational advances, feature selection and diabetes proteomics. In this study, we systematically review the computational advances of label-free quantification and diabetic marker selection methods which were applied to get the understanding of DM pathological mechanisms. Firstly, different popular quantification measurements and proteomic quantification software tools which have been applied to the diabetes studies are comprehensively discussed. Secondly, a number of popular manipulation methods including transformation, pretreatment (centering, scaling, and normalization), missing value imputation methods and a variety of popular feature selection techniques applied to diabetes proteomic data are overviewed with objective evaluation on their advantages and disadvantages. Finally, the guidelines for the efficient use of the computationbased LFQ technology and feature selection methods in diabetes proteomics are proposed. In summary, this review provides guidelines for researchers who will engage in proteomics biomarker discovery and by properly applying these proteomic computational advances, more reliable therapeutic targets will be found in the field of diabetes mellitus.

Imputation Analysis for Time Series Air Quality (PM10) Data Set: A Comparison of Several Methods

Good quality data is important to guarantee for the best quality results of research analysis. However, the quality of the data often being impacted by the existence of missing values that bring bad implication on the accuracy of analysis and subsequently lead to biased results. In air quality data set, missing values problem often caused by various reasons, for example machine malfunction and errors, computer system crashes, human error and insufficient sampling used. In the case for time series modelling, complete series of data is very important to enable for the model construction. This paper aims to highlight a systematic statistical procedure and analysis on how to investigate the performance of several missing values imputation methods to solve for the problem of missing value existence when data are time series. The knowledge could help researchers to implement a comprehensive procedure in deciding a type of imputation method that suits with their data. A case study was conducted using real data set from Shah Alam air quality monitoring station. The results have shown that the missing data at the monitoring station is completely at random (MCAR). Among six imputation methods compared and based on the performance of indicators such as RMSE, MAE, AI and R2 it is shown that imputation using Kalman Filter using ARIMA model is the best appropriate method for the data set.