Percentage Of Missing Data Research Articles

National Fatality Review Case Reporting System: Twenty Years of Data Collection.

The National Fatality Review Case Reporting System (NFR-CRS) is a web-based data collection tool for child death review and fetal and infant mortality review teams. The NFR-CRS captures information from the multidisciplinary review, including the social and community risk factors that may have impacted the death. The NFR-CRS is a nimble data system that has evolved throughout the past 20 years. The most recent enhancements include a life stressors section focused on collecting contextual information such as racism and poverty, revised cause of death sections to better align data collected with risk factors identified in the research, and enhanced data visualizations. The NFR-CRS has improved data quality since the launch of a data quality initiative in 2015. As a result of the data quality initiative, the completeness, consistency, accuracy, and timeliness of the NFR-CRS data have improved. Limitations for the NFR-CRS include the fact that data entered are not population-based, data fields have evolved over time, and there are high percentages of missing and unknown data. Despite its limitations, the NFR-CRS remains a valuable research tool, especially when paired with other data sources.

An efficient improved singular spectrum analysis for processing GNSS position time series with missing data

Summary The improved SSA (ISSA) method is widely recognized for directly extracting signals from gappy time series without requiring prior interpolation. However, it is rather time consuming, particularly for long time series with large window sizes, such as Global Navigation Satellite System (GNSS) position time series. This study proposes an efficient ISSA method that yields equivalent results to the ISSA method while significantly reducing computation time. Both methods aim to minimize the quadratic norm of principal components, while our method has fewer unknown parameters in the principal component computation than those of the ISSA method. We evaluate the performance of the proposed method using real GNSS position time series from 27 permanent stations located in mainland China. Results show that the proposed method can effectively reduce computation time than the ISSA method and the improvement depends on the chosen window size, the time series length, and the percentage of missing data. This efficient approach can be naturally extended to principal component analysis (PCA) and multi-channel SSA (MSSA) for processing multiple incomplete time series, improving computational efficiencies compared to the modified PCA and the improved MSSA while maintaining unchanged results. We also compare the ISSA method with the modified SSA (SSAM) and the iterative SSA methods using both real and synthetic time series data. Results indicate that the ISSA method outperforms the SSAM method, and when conducted iteratively, also surpasses the iterative SSA method.

An Integer Programming Approach to Subspace Clustering with Missing Data

In the subspace clustering with missing data (SCMD) problem, we are given a collection of n partially observed d-dimensional vectors. The data points are assumed to be concentrated near a union of low-dimensional subspaces. The goal of SCMD is to cluster the vectors according to their subspace membership and recover the underlying basis, which can then be used to infer their missing entries. State-of-the-art algorithms for SCMD can fail on instances with a high proportion of missing data, with full-rank data, or if the underlying subspaces are similar to each other. We propose a novel integer programming approach for SCMD. The approach is based on dynamically determining a set of candidate subspaces and optimally assigning points to selected subspaces. The problem structure is identical to the classical facility-location problem, with subspaces playing the role of facilities and data points playing that of customers. We propose a column-generation approach for identifying candidate subspaces combined with a Benders decomposition approach for solving the linear programming relaxation of the formulation. An empirical study demonstrates that the proposed approach can achieve better clustering accuracy than state-of-the-art methods when the data are high rank, the percentage of missing data is high, or the subspaces are similar. Funding: Support for this research was provided by American Family Insurance through a research partnership with the University of Wisconsin–Madison’s Data Science Institute. Supplemental Material: The online appendix is available at https://doi.org/10.1287/ijoo.2023.0020 .

Data‐driven Buck converter model identification method with missing outputs

AbstractA data‐driven Buck converter model identification method is proposed to deal with missing (incomplete) outputs, which is robust to the data length and percentage of missing data. A nuclear norm based convex optimization problem instead of linear interpolation, to guarantee the recovered missing data satisfying the potential model structured low‐rank character, is constructed to estimate missing outputs. The alternating direction method of multiplier strategy is used to solve the nuclear norm based convex optimization problem. In this way, the high‐quality missing data can be estimated, even for short data length and high percentage of missing data. Based on the recovered data, the subspace identification method provides accurate estimates of the structure and parameter of the Buck converter synchronously. By applying the proposed method to a Buck converter, experimental results demonstrate its effectiveness.

Assessing Drought Vulnerability in the Brazilian Atlantic Forest Using High-Frequency Data

This research investigates the exposure of plant species to extreme drought events in the Brazilian Atlantic Forest, employing an extensive dataset collected from 205 automatic weather stations across the region. Meteorological indicators derived from hourly data, encompassing precipitation and maximum and minimum air temperature, were utilized to quantify past, current, and future drought conditions. The dataset, comprising 10,299,236 data points, spans a substantial temporal window and exhibits a modest percentage of missing data. Missing data were excluded from analysis, aligning with the decision to refrain from using imputation methods due to potential bias. Drought quantification involved the computation of the aridity index, the analysis of consecutive hours without precipitation, and the classification of wet and dry days per month. Mann–Kendall trend analysis was applied to assess trends in evapotranspiration and maximum air temperature, considering their significance. The hazard assessment, incorporating environmental factors influencing tree growth dynamics, facilitated the ranking of meteorological indicators to identify regions most exposed to drought events. The results revealed consistent occurrences of extreme rainfall events, indicated by positive outliers in monthly precipitation values. However, significant trends were observed, including an increase in daily maximum temperature and consecutive hours without precipitation, coupled with a decrease in daily precipitation across the Brazilian Atlantic Forest. No significant correlation between vulnerability ranks and weather station latitudes and elevation were found, suggesting that geographical location and elevation do not strongly influence observed dryness trends.

Solving the missing value problem in PCA by Orthogonalized-Alternating Least Squares (O-ALS)

Dealing with missing data poses a challenge in Principal Component Analysis (PCA) since the most common algorithms are not designed to handle them. Several approaches have been proposed to solve the missing value problem in PCA, such as Imputation based on SVD (I-SVD), where missing entries are filled by imputation and updated in every iteration until convergence of the PCA model, and the adaptation of the Nonlinear Iterative Partial Least Squares (NIPALS) algorithm, able to work skipping the missing entries during the least-squares estimation of scores and loadings. However, some limitations have been reported for both approaches. On the one hand, convergence of the I-SVD algorithm can be very slow for data sets with a high percentage of missing data. On the other hand, the orthogonality properties among scores and loadings might be lost when using NIPALS.To solve these issues and perform PCA of data sets with missing values without the need of imputation steps, a novel algorithm called Orthogonalized-Alternating Least Squares (O-ALS) is proposed. The O-ALS algorithm is an alternating least-squares algorithm that estimates the scores and loadings subject to the Gram-Schmidt orthogonalization constraint. The way to estimate scores and loadings is adapted to work only with the available information.In this study, the performance of O-ALS is tested and compared with NIPALS and I-SVD in simulated data sets and in a real case study. The results show that O-ALS is an accurate and fast algorithm to analyze data with any percentage and distribution pattern of missing entries, being able to provide correct scores and loadings in cases where I-SVD and NIPALS do not perform satisfactorily.

Performance of Different Imputation Methods in Logistic Regression with Multicollinearity

The existence of missing values in the data, which must be taken into account, is an important issue in research. In particular, in the case of medical data, missing data can lead to a loss of trust in patient evaluation and make it impossible to classify people according to their level of health or disease. Furthermore, multicollinearity between independent variables can lead to misleading results. Therefore, the present research aims to study the efficiency of missing data imputation techniques for logistic regression with complete multicollinearity. The missing data imputation methods considered in this research were MEAN (mean imputation), MI (multiple imputation), KNN (k-nearest neighbor imputation), RF (random forest imputation), SRI (stochastic regression imputation), and BRI (Bayesian linear regression imputation). In this study, the simulation conducted was done with sample sizes of 20, 50, 100, 150, 200, 500, and 1000, as well as percentages of missing data at 10, 20, 30, and 40%. To compare efficiency, the EMSE (estimated mean square error) was used. The results showed that the RF method was most effective when the sample size was large and with a high percentage of missing data, whereas the MEAN method had the worst performance in all cases. When the sample size is small with a high proportion of missing data, KNN might be a preferable alternative for imputation. The inquiry into expanding the number of independent variables and different patterns of multicollinearity may be essential for future work.

Equivalence of variance components between standard and recursive genetic models using LDL′ transformations

BackgroundRecursive models are a category of structural equation models that propose a causal relationship between traits. These models are more parameterized than multiple trait models, and they require imposing restrictions on the parameter space to ensure statistical identification. Nevertheless, in certain situations, the likelihood of recursive models and multiple trait models are equivalent. Consequently, the estimates of variance components derived from the multiple trait mixed model can be converted into estimates under several recursive models through LDL′ or block-LDL′ transformations.ResultsThe procedure was employed on a dataset comprising five traits (birth weight—BW, weight at 90 days—W90, weight at 210 days—W210, cold carcass weight—CCW and conformation—CON) from the Pirenaica beef cattle breed. These phenotypic records were unequally distributed among 149,029 individuals and had a high percentage of missing data. The pedigree used consisted of 343,753 individuals. A Bayesian approach involving a multiple-trait mixed model was applied using a Gibbs sampler. The variance components obtained at each iteration of the Gibbs sampler were subsequently used to estimate the variance components within three distinct recursive models.ConclusionsThe LDL′ or block-LDL′ transformations applied to the variance component estimates achieved from a multiple trait mixed model enabled inference across multiple sets of recursive models, with the sole prerequisite of being likelihood equivalent. Furthermore, the aforementioned transformations simplify the handling of missing data when conducting inference within the realm of recursive models.

Comparison of ddRADseq and EUChip60K SNP genotyping systems for population genetics and genomic selection in Eucalyptus dunnii (Maiden).

Eucalyptus dunnii is one of the most important Eucalyptus species for short-fiber pulp production in regions where other species of the genus are affected by poor soil and climatic conditions. In this context, E. dunnii holds promise as a resource to address and adapt to the challenges of climate change. Despite its rapid growth and favorable wood properties for solid wood products, the advancement of its improvement remains in its early stages. In this work, we evaluated the performance of two single nucleotide polymorphism, (SNP), genotyping methods for population genetics analysis and Genomic Selection in E. dunnii. Double digest restriction-site associated DNA sequencing (ddRADseq) was compared with the EUChip60K array in 308 individuals from a provenance-progeny trial. The compared SNP set included 8,011 and 19,008 informative SNPs distributed along the 11 chromosomes, respectively. Although the two datasets differed in the percentage of missing data, genome coverage, minor allele frequency and estimated genetic diversity parameters, they revealed a similar genetic structure, showing two subpopulations with little differentiation between them, and low linkage disequilibrium. GS analyses were performed for eleven traits using Genomic Best Linear Unbiased Prediction (GBLUP) and a conventional pedigree-based model (ABLUP). Regardless of the SNP dataset, the predictive ability (PA) of GBLUP was better than that of ABLUP for six traits (Cellulose content, Total and Ethanolic extractives, Total and Klason lignin content and Syringyl and Guaiacyl lignin monomer ratio). When contrasting the SNP datasets used to estimate PAs, the GBLUP-EUChip60K model gave higher and significant PA values for six traits, meanwhile, the values estimated using ddRADseq gave higher values for three other traits. The PAs correlated positively with narrow sense heritabilities, with the highest correlations shown by the ABLUP and GBLUP-EUChip60K. The two genotyping methods, ddRADseq and EUChip60K, are generally comparable for population genetics and genomic prediction, demonstrating the utility of the former when subjected to rigorous SNP filtering. The results of this study provide a basis for future whole-genome studies using ddRADseq in non-model forest species for which SNP arrays have not yet been developed.

Enhancing environmental data imputation: A physically-constrained machine learning framework

In water resources management, new computational capabilities have made it possible to develop integrated models to jointly analyze climatic conditions and water quantity/quality of the entire watershed system. Although the value of this integrated approach has been demonstrated so far, the limited availability of field data may hinder its applicability by causing high uncertainty in the model response. In this context, before collecting additional data, it is recommended first to recognize what improvement in model performance would occur if all available records could be well exploited. This work proposes a novel machine learning framework with physical constraints capable of successfully imputing a high percentage of missing data belonging to several environmental domains (meteorology, water quantity, water quality), yielding satisfactory results. In particular, the minimum NSE computed for meteorologic variables is 0.72. For hydrometric variables, NSE is always >0.97. More than 78 % of the physical-water-quality variables is characterized by NSE > 0.45, and >66 % of the chemical-water quality variables reaches NSE > 0.35. This work's results demonstrate the proposed framework's effectiveness as a data augmentation tool to improve the performance of integrated environmental modeling.

Synergising Simulated Annealing and Generative Adversarial Network for Enhanced Wind Data Imputation in Climate Change Modelling

Climate models help us simulate and predict how the Earth’s climate is going to change in the future. Wind speed data is critical for developing and validating such models. However, in the real world, often owing to many factors such as station maintenance and sensor failures, a considerable amount of wind data goes missing. The Generative Adversarial Network (GAN) has been used to impute missing wind data, but the handling of unrealistic GAN output has remained largely unstudied. In this paper, we propose a novel hybrid approach that combines both the GAN and dual annealing algorithms to not only impute missing wind speed data but also counter unrealistic GAN outcomes. The hourly mean wind data has been collected from the National Centers for Environmental Information for four Indian stations, viz. Ahmedabad, Indore, Mangaluru and Mumbai. We compared the performance of the proposed approach with those of k-nn, soft imputation, and plain GAN-based approaches on mean, variance, standard deviation, kurtosis, skewness, and R-square. We found that our approach ranks number one based on the R-square value for all the considered stations. Our model consistently produces realistic results, unlike plain GAN. We observed that Mumbai has the lowest percentage of missing data (13.14%) and the highest R-square value (0.9999186451). However, Indore has the highest percentage of missing data (46.6463%) and the lowest R-square value (0.9046885604).

Extraction of periodic signals in Global Navigation Satellite System (GNSS) vertical coordinate time series using the adaptive ensemble empirical modal decomposition method

Abstract. Empirical modal decomposition (EMD) is an efficient tool for extracting a signal from stationary or non-stationary time series and is enhanced in stability and robustness by ensemble empirical mode decomposition (EEMD). Adaptive EEMD further improves computational efficiency through adaptability in the white noise amplitude and set average number. However, its effectiveness in the periodic signal extraction in Global Navigation Satellite System (GNSS) coordinate time series regarding the inevitable missing data and offset issues has not been comprehensively validated. In order to thoroughly investigate their impacts, we simulated 5 years of daily time series data with different missing data percentages or a different number of offsets and conducted them 300 times for each simulation. The results show that high accuracy could reach the overall random missing rate below 15 % and avoid consecutive misses exceeding 30 d. Meanwhile, offsets should be corrected in advance regardless of their magnitudes. The analysis of the vertical components of 13 stations within the Australian Global Sea Level Observing System (GLOSS) monitoring network demonstrates the advantage of adaptive EEMD in revealing the time-varying characteristics of periodic signals. From the perspectives of correlation coefficients (CCs), root mean square error (RMSE), power spectral density indices (κ) and signal-to-noise ratio (SNR), the means for adaptive EEMD are 0.36, 0.81, −0.18 and 0.48, respectively, while for least squares (LS), they are 0.27, 0.86, −0.50 and 0.23. Meanwhile, a significance test of the residuals further substantiates the effectiveness in periodic signal extraction, which shows that there is no annual signal remaining. Also, the longer the series, the higher the accuracy of the reasonable extracted periodic signal concluded via the significance test. Moreover, driving factors are more effectively facilitated by the time-varying periodic characteristics compared with the constant periodic signal derived by LS. Overall, the application of adaptive EEMD could achieve high accuracy in analyzing GNSS time series, but it should be based on properly dealing with missing data and offsets.

Temporal Trends in the Completeness of Epidemiological Variables in a Hospital-Based Cancer Registry of a Pediatric Oncology Center in Brazil.

This ecological time series study aimed to examine the temporal trends in the completeness of epidemiological variables from a hospital-based cancer registry (HbCR) of a reference center for pediatric oncology in Brazil from 2010 to 2016. Completeness categories were based on the percentage of missing data, with the categories excellent (<5%), good (5-10%), regular (11-20%), poor (21-50%), and very poor (>50%). Descriptive and bivariate analyses were performed using R.4.1.0; a Mann-Kendall trend test was performed to examine the temporal trends. Variables with the highest incompleteness included race/color (17.24% in 2016), level of education (51.40% in 2015), TNM (56.88% in 2012), disease status at the end of the first treatment (12.09% in 2013), cancer family history (79.12% in 2013), history of alcoholic consumption (39.25% in 2015), history of tobacco consumption (38.32% in 2015), and type of admission clinic (10.28% in 2015). Nevertheless, most variables achieved 100% completeness and were classified as excellent across the time series. A significant trend was observed for race/color, TNM, and history of tobacco consumption. While most variables maintained excellent completeness, the increasing incompleteness trend in race/color and decreasing trend in TNM underscore the importance of reliable and complete HbCRs for personalized cancer care, for planning public policies, and for conducting research on cancer control.

Near-Surface Air Temperature Records over the Past 30 Years in the Interior of Dronning Maud Land, East Antarctica

Abstract The interior of Dronning Maud Land (DML) in East Antarctica is one of the most data-sparse regions of Antarctica for studying climate change. A monthly mean near-surface temperature dataset for the last 30 years has been compiled from the historical records from automatic weather stations (AWSs) at three sites in the region (Mizuho, Relay Station, and Dome Fuji). Multiple AWSs have been installed along the route to Dome Fuji since the 1990s, and observations have continued to the present day. The use of passive-ventilated radiation shields for the temperature sensors at the AWSs may have caused a warm bias in the temperature measurements, however, due to insufficient ventilation in the summer, when solar radiation is high and winds are low. In this study, these warm biases are quantified by comparison with temperature measurements with an aspirated shield and subsequently removed using a regression model. Systematic error resulting from changes in the sensor height due to accumulating snow was insignificant in our study area. Several other systematic errors occurring in the early days of the AWS systems were identified and corrected. After the corrections, multiple AWS records were integrated to create a time series for each station. The percentage of missing data over the three decades was 21% for Relay Station and 28% for Dome Fuji. The missing rate at Mizuho was 49%, more than double that at Relay Station. These new records allow for the study of temperature variability and change in DML, where climate change has so far been largely unexplored. Significance Statement Antarctic climate change has been studied using temperature data at staffed stations. The staffed stations, however, are mainly located on the Antarctic Peninsula and in the coastal regions. Climate change is largely unknown in the Antarctic plateau, particularly in the western sector of the East Antarctic Plateau in areas such as the interior of Dronning Maud Land (DML). To fill the data gap, this study presents a new dataset of monthly mean near-surface climate data using historical observations from three automatic weather stations (AWSs). This dataset allows us to study temperature variability and change over a data-sparse region where climate change has been largely unexplored.

Genetic Diversity within a Collection of Italian Maize Inbred Lines: A Resource for Maize Genomics and Breeding

Genetic diversity is fundamental for studying the complex architecture of the traits of agronomic importance, controlled by major and minor loci. Moreover, well-characterized germplasm collections are essential tools for dissecting and analyzing genetic and phenotypic diversity in crops. A panel of 360 entries, a subset of a larger collection maintained within the GenBank at CREA Bergamo, which includes the inbreds derived from traditional Italian maize open-pollinated (OP) varieties and advanced breeding ones (Elite Inbreds), was analyzed to identify SNP markers using the tGBS® genotyping-by-sequencing technology. A total of 797,368 SNPs were found during the initial analysis. Imputation and filtering processes were carried out based on the percentage of missing data, redundant markers, and rarest allele frequencies, resulting in a final dataset of 15,872 SNP markers for which a physical map position was identified. Using this dataset, the inbred panel was characterized for linkage disequilibrium (LD), genetic diversity, population structure, and genetic relationships. LD decay at a genome-wide level indicates that the collection is a suitable resource for association mapping. Population structure analyses, which were carried out with different clustering methods, showed stable grouping statistics for four groups, broadly corresponding to ‘Insubria’, ‘Microsperma’, and ‘Scagliolino’ genotypes, with a fourth group composed prevalently of elite accessions derived from Italian and US breeding programs. Based on these results, the CREA Italian maize collection, genetically characterized in this study, can be considered an important tool for the mapping and characterization of useful traits and associated loci/alleles, to be used in maize breeding programs.

A framework for block-wise missing data in multi-omics.

High-throughput technologies have generated vast amounts of omic data. It is a consensus that the integration of diverse omics sources improves predictive models and biomarker discovery. However, managing multiple omics data poses challenges such as data heterogeneity, noise, high-dimensionality and missing data, especially in block-wise patterns. This study addresses the challenges of high dimensionality and block-wise missing data through a regularization and constrained-based approach. The methodology is implemented in the R package bwm for binary and continuous response variables, and applied to breast cancer and exposome multi-omics datasets, achieving strong performance even in scenarios with missing data present in all omics. In binary classification task, our proposed model achieves accuracy in the range of 86% to 92%, and F1 in the range of 68% to 79%. And, in regression task the correlation between true and predicted responses is in the range of 72% to 76%. However, there is a slight decline in performance metrics as the percentage of missing data increases. In scenarios where block-wise missing data affects multiple omics, the model performance actually surpasses that of scenarios where missing data is present in only one omics. One possible explanation for this might be that the other scenarios introduce a greater diversity of observation profiles, leading to a more robust model. Depending on the specific omics being studied, there is greater consistency in feature selection when comparing block-wise missing data scenarios.

Data Augmentation with Cross-Modal Variational Autoencoders (DACMVA) for Cancer Survival Prediction.

The ability to translate Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs) into different modalities and data types is essential to improve Deep Learning (DL) for predictive medicine. This work presents DACMVA, a novel framework to conduct data augmentation in a cross-modal dataset by translating between modalities and oversampling imputations of missing data. DACMVA was inspired by previous work on the alignment of latent spaces in Autoencoders. DACMVA is a DL data augmentation pipeline that improves the performance in a downstream prediction task. The unique DACMVA framework leverages a cross-modal loss to improve the imputation quality and employs training strategies to enable regularized latent spaces. Oversampling of augmented data is integrated into the prediction training. It is empirically demonstrated that the new DACMVA framework is effective in the often-neglected scenario of DL training on tabular data with continuous labels. Specifically, DACMVA is applied towards cancer survival prediction on tabular gene expression data where there is a portion of missing data in a given modality. DACMVA significantly (p << 0.001, one-sided Wilcoxon signed-rank test) outperformed the non-augmented baseline and competing augmentation methods with varying percentages of missing data (4%, 90%, 95% missing). As such, DACMVA provides significant performance improvements, even in very-low-data regimes, over existing state-of-the-art methods, including TDImpute and oversampling alone.

Consumption prediction with privacy concern: Application and evaluation of Federated Learning

In this paper, we propose privacy-friendly electricity consumption prediction models based on Federated Learning (FL). Federated Learning provides a novel framework for Artificial Neural Network training. It decouples data storage from model training from decentralized data sources. A central server does not need to collect raw training data from individual clients but only the model parameters, like the gradients or weights updates. The sensitive raw data can be safely stored and used by end-users themselves. We improve the accuracy of the FL model by first clustering households, and training a personalized model for each cluster. We also analyse the Deep Leakage from Gradients (DLG) attack in our study case with three scenarios. Our simulations suggest that the DLG attack can barely succeed in consumption prediction. In this way, the FL model can guarantee clients’ privacy by design. We use a large-scale dataset that contains 3590 households’ 1.5 years of consumption to test the FL model’s performance. Several clustering algorithms are tested for the following experiments. To comprehensively test the FL model’s performance, we propose several popular neural network models: the simple Deep Neural Network, Long Short-Term Memory, Convolutional Neural Network, and WaveNet. These models are both trained under the centralized and federated framework. The federated trained models slightly sacrifice the model’s accuracy while guaranteeing the client’s data privacy. Meanwhile, the federated model shows remarkable scalability. Under the FL framework, new clients can obtain their prediction 6 times faster. Moreover, the federated model has strong robustness against missing or damaged training data. With a certain percentage of missing data in the training set, the centralized model’s accuracy gets 27% worse, while the federated model’s accuracy only gets 2% worse.

A Conceptual Framework of Data Readiness for the Health and Aging Brain Study‐Health Disparities

AbstractBackgroundThe Health and Aging Brain Study: Health Disparities (HABS‐HD) seeks to understand the biological, social and environmental factors that impact brain aging among diverse communities. HABS‐HD, like many other NIH funded data‐sharing projects, has important data assets for various uses, including social, environmental and behavioral data, and multiple data flow pathways. Machine learning (ML) develops algorithms and models to continuously improve itself over time, but the determination of data quality and its readiness are needed for these models to operate efficiently. Therefore, developing a data readiness reporting methodology has become a very urgent task for HABS‐HD.MethodIn this study, we developed a conceptual framework of data readiness. First, we analyzed the missing data percentage and used ML‐Based Multiple Imputation (MLMI) for missing data imputation. Then, we performed SVM based on Recursive Feature Elimination and Cross Validation (SVM‐RFE‐CV) for feature elimination and outlier removal. Lastly, we rated the data readiness based on the three metrics: missing data percentage, performance before feature engineering, and performance after feature engineering to rate data readiness. All the three scores were averaged to rate the overall readiness of data.ResultA framework for calculating overall average score for readiness of data was presented (1 stands for completely accessible, 0 for not accessible at all, and 0.5 for neutral). Our results show that the framework of data readiness was straightforward and useful in assessing how ready the HABS‐HD data is for ML.ConclusionThe systematic analysis of readiness of data before building ML models is of utmost importance. And it has a significant impact on biomarker discovery and disease prediction application for Alzheimer’s disease. The conceptual framework of data readiness works well for our Alzheimer’s disease models in HABS‐HD. It can also be applied to other disease data readiness reporting.

Estimation of rainfall and streamflow missing data under uncertainty for Nile basin headwaters: the case of Ghba catchments

The datasets in semi-arid and arid regions of developing countries are known for their long period of missing data. To approximate any missing data interpolation or estimation techniques are frequently used. The current study focuses on applying the concept of data uncertainty by setting out different percentages of missing data from the known dataset at all target stations calibrated and validated against the ensemble simulation interpolation and reconstitution approach by contrasting findings with voluntarily excluded data that will be re-estimated using the various approaches selected. While testing the nearby stations (6 to 50 km) for filling the missing data were used. The tests on the selected stations shows that the better estimates of precipitation and streamflow are the ID and the NR methods. Thus, the methods that revealed a better estimation were used for filling in missing data of the respective stations to undergo a water resource study.