K-nearest Neighbor Imputation Research Articles

Analyzing Monthly Blood Test Data to Forecast 30-Day Hospital Readmissions among Maintenance Hemodialysis Patients.

Background: The increase in the global population of hemodialysis patients is linked to aging demographics and the prevalence of conditions such as arterial hypertension and diabetes mellitus. While previous research in hemodialysis has mainly focused on mortality predictions, there is a gap in studies targeting short-term hospitalization predictions using detailed, monthly blood test data. Methods: This study employs advanced data preprocessing and machine learning techniques to predict hospitalizations within a 30-day period among hemodialysis patients. Initial steps include employing K-Nearest Neighbor (KNN) imputation to address missing data and using the Synthesized Minority Oversampling Technique (SMOTE) to ensure data balance. The study then applies a Support Vector Machine (SVM) algorithm for the predictive analysis, with an additional enhancement through ensemble learning techniques, in order to improve prediction accuracy. Results: The application of SVM in predicting hospitalizations within a 30-day period among hemodialysis patients resulted in an impressive accuracy rate of 93%. This accuracy rate further improved to 96% upon incorporating ensemble learning methods, demonstrating the efficacy of the chosen machine learning approach in this context. Conclusions: This study highlights the potential of utilizing machine learning to predict hospital readmissions within a 30-day period among hemodialysis patients based on monthly blood test data. It represents a significant leap towards precision medicine and personalized healthcare for this patient group, suggesting a paradigm shift in patient care through the proactive identification of hospitalization risks.

Predicting major adverse cardiovascular events after orthotopic liver transplantation using a supervised machine learning model: A cohort study.

Liver transplant (LT) patients have become older and sicker. The rate of post-LT major adverse cardiovascular events (MACE) has increased, and this in turn raises 30-d post-LT mortality. Noninvasive cardiac stress testing loses accuracy when applied to pre-LT cirrhotic patients. To assess the feasibility and accuracy of a machine learning model used to predict post-LT MACE in a regional cohort. This retrospective cohort study involved 575 LT patients from a Southern Brazilian academic center. We developed a predictive model for post-LT MACE (defined as a composite outcome of stroke, new-onset heart failure, severe arrhythmia, and myocardial infarction) using the extreme gradient boosting (XGBoost) machine learning model. We addressed missing data (below 20%) for relevant variables using the k-nearest neighbor imputation method, calculating the mean from the ten nearest neighbors for each case. The modeling dataset included 83 features, encompassing patient and laboratory data, cirrhosis complications, and pre-LT cardiac assessments. Model performance was assessed using the area under the receiver operating characteristic curve (AUROC). We also employed Shapley additive explanations (SHAP) to interpret feature impacts. The dataset was split into training (75%) and testing (25%) sets. Calibration was evaluated using the Brier score. We followed Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis guidelines for reporting. Scikit-learn and SHAP in Python 3 were used for all analyses. The supplementary material includes code for model development and a user-friendly online MACE prediction calculator. Of the 537 included patients, 23 (4.46%) developed in-hospital MACE, with a mean age at transplantation of 52.9 years. The majority, 66.1%, were male. The XGBoost model achieved an impressive AUROC of 0.89 during the training stage. This model exhibited accuracy, precision, recall, and F1-score values of 0.84, 0.85, 0.80, and 0.79, respectively. Calibration, as assessed by the Brier score, indicated excellent model calibration with a score of 0.07. Furthermore, SHAP values highlighted the significance of certain variables in predicting postoperative MACE, with negative noninvasive cardiac stress testing, use of nonselective beta-blockers, direct bilirubin levels, blood type O, and dynamic alterations on myocardial perfusion scintigraphy being the most influential factors at the cohort-wide level. These results highlight the predictive capability of our XGBoost model in assessing the risk of post-LT MACE, making it a valuable tool for clinical practice. Our study successfully assessed the feasibility and accuracy of the XGBoost machine learning model in predicting post-LT MACE, using both cardiovascular and hepatic variables. The model demonstrated impressive performance, aligning with literature findings, and exhibited excellent calibration. Notably, our cautious approach to prevent overfitting and data leakage suggests the stability of results when applied to prospective data, reinforcing the model's value as a reliable tool for predicting post-LT MACE in clinical practice.

EvoImp: Multiple Imputation of Multi-label Classification data with a genetic algorithm.

Missing data is a prevalent problem that requires attention, as most data analysis techniques are unable to handle it. This is particularly critical in Multi-Label Classification (MLC), where only a few studies have investigated missing data in this application domain. MLC differs from Single-Label Classification (SLC) by allowing an instance to be associated with multiple classes. Movie classification is a didactic example since it can be "drama" and "bibliography" simultaneously. One of the most usual missing data treatment methods is data imputation, which seeks plausible values to fill in the missing ones. In this scenario, we propose a novel imputation method based on a multi-objective genetic algorithm for optimizing multiple data imputations called Multiple Imputation of Multi-label Classification data with a genetic algorithm, or simply EvoImp. We applied the proposed method in multi-label learning and evaluated its performance using six synthetic databases, considering various missing values distribution scenarios. The method was compared with other state-of-the-art imputation strategies, such as K-Means Imputation (KMI) and weighted K-Nearest Neighbors Imputation (WKNNI). The results proved that the proposed method outperformed the baseline in all the scenarios by achieving the best evaluation measures considering the Exact Match, Accuracy, and Hamming Loss. The superior results were constant in different dataset domains and sizes, demonstrating the EvoImp robustness. Thus, EvoImp represents a feasible solution to missing data treatment for multi-label learning.

Privacy-Preserving Vertical Federated KNN Feature Imputation Method

Federated learning stands as a pivotal component in the construction of data infrastructure. It significantly fortifies the safety and reliability of data circulation links, facilitating credible sharing and openness among diverse subjects. The presence of missing data poses a pervasive and challenging issue in the implementation of federated learning. Current research on imputation missing values predominantly concentrates on centralized methods and horizontal federation scenarios. However, there is a notable absence of exploration in the context of vertical federated application scenarios. In this paper, the problem of missing imputation in vertical federated learning is investigated and a novel vertical federated k-nearest neighbors (KNN) imputation method is proposed. Extensive experiments are conducted using publicly available data sets to compare existing imputation methods, the results demonstrate the effectiveness and progress of our approach.

결측치를 포함한 데이터의 k-평균 군집분석 방법 비교

Cluster analysis is an unsupervised learning method to find heterogeneous clusters that capture similarities among items and separate different items into different clusters. Various cluster analysis techniques have been proposed, and the k-means clustering method, which minimizes the sum of Euclidean distances between cluster centroids and individual entities, is widely recognized as a standard cluster analysis method. When data include missing values, it is challenging to conduct cluster analysis, because it is impossible to calculate distances between centroids of clusters and incomplete items, resulting in excluding classification of these items. Techniques have been suggested to handle missing values in k-means clustering, including conducting cluster analysis after imputation of missing values or cluster analysis based on available information. In this study, we explore methods to perform k-means cluster analysis on data with missing values and evaluate performance of these methods using a simulation. The results of simulation studies indicate that conducting k-means cluster analysis after imputation yields the better performance than the one based on available information. Among the various imputation methods, k-nearest neighbors imputation performed the best.

K-Means Clustering with KNN and Mean Imputation on CPU Benchmark Compilation Data

In the rapidly evolving digital age, data is becoming a valuable source for decision-making and analysis. Clustering, as an important technique in data analysis, has a key role in organizing and understanding complex datasets. One of the effective clustering algorithms is k-means. However, this algorithm is prone to the problem of missing values, which can significantly affect the quality of the resulting clusters. To overcome this challenge, imputation methods are used, including mean imputation and K-Nearest Neighbor (KNN) imputation. This study aims to analyze the impact of imputation methods on CPU Benchmark Compilation clustering results. Evaluation of the clustering results using the silhouette coefficient showed that clustering with mean imputation achieved a score of 0.782, while with KNN imputation it achieved a score of 0.777. In addition, the cluster interpretation results show that the KNN method produces more information that is easier for users to understand. This research provides valuable insights into the effectiveness of imputation methods in improving the quality of data clustering results in assisting CPU selection decisions on CPU Benchmark Compilation data.

Application of Imputation Method for Compositional Data with Missing Values based on Adaptive LASSO Model: the Composition of Employment Industry in Taiyuan, China

The tripartite industry classification, which divides all economic activities into three parts, is a classification method to reflect the dynamic process of economic development and the historical trend of the change of resource allocation structure.The fact shows that the proportion of each industry has become an important symbol of the level of national economic development. The proportion of each industry is compositional data,which is a kind of complex multidimensional data used in many fields. All components in the compositional data are non-negative and carry only relative information. In practice, there could be missing values in compositional data. However, general statistical analysis methods cannot be firstly used for compositional data with missing values. The complexity of the missing value of compositional data makes traditional imputation methods no longer suitable. Thus, how to carry out effective statistical inference for compositional data with missing values attracts the attention of many scholars, recently. In this paper, we focus on the imputation problem in compositional data containing missing values, and propose an Adaptive Least Absolute Shrinkage and Selection Operator (ALASSO) imputation method to obtain a complete datasets through variable selection and parameter estimation. Then, the new method is simulated and empirically analyzed, and a comparative study with mean imputation, k-nearest neighbor imputation, and iterative regression imputation is conducted. The results show that the ALASSO imputation method has the highest accuracy for different missing rates, dimensions and correlation coefficients.

Cardiac disease risk prediction using machine learning algorithms

AbstractHeart attack is a life‐threatening condition which is mostly caused due to coronary disease resulting in death in human beings. Detecting the risk of heart diseases is one of the most important problems in medical science that can be prevented and treated with early detection and appropriate medical management; it can also help to predict a large number of medical needs and reduce expenses for treatment. Predicting the occurrence of heart diseases by machine learning (ML) algorithms has become significant work in healthcare industry. This study aims to create a such system that is used for predicting whether a patient is likely to develop heart attacks, by analysing various data sources including electronic health records and clinical diagnosis reports from hospital clinics. ML is used as a process in which computers learn from data in order to make predictions about new datasets. The algorithms created for predictive data analysis are often used for commercial purposes. This paper presents an overview to forecast the likelihood of a heart attack for which many ML methodologies and techniques are applied. In order to improve medical diagnosis, the paper compares various algorithms such as Random Forest, Regression models, K‐nearest neighbour imputation (KNN), Naïve Bayes algorithm etc. It is found that the Random Forest algorithm provides a better accuracy of 88.52% in forecasting heart attack risk, which could herald a revolution in the diagnosis and treatment of cardiovascular illnesses.

Missing data imputation, prediction, and feature selection in diagnosis of vaginal prolapse

BackgroundData loss often occurs in the collection of clinical data. Directly discarding the incomplete sample may lead to low accuracy of medical diagnosis. A suitable data imputation method can help researchers make better use of valuable medical data.MethodsIn this paper, five popular imputation methods including mean imputation, expectation-maximization (EM) imputation, K-nearest neighbors (KNN) imputation, denoising autoencoders (DAE) and generative adversarial imputation nets (GAIN) are employed on an incomplete clinical data with 28,274 cases for vaginal prolapse prediction. A comprehensive comparison study for the performance of these methods has been conducted through certain classification criteria. It is shown that the prediction accuracy can be greatly improved by using the imputed data, especially by GAIN. To find out the important risk factors to this disease among a large number of candidate features, three variable selection methods: the least absolute shrinkage and selection operator (LASSO), the smoothly clipped absolute deviation (SCAD) and the broken adaptive ridge (BAR) are implemented in logistic regression for feature selection on the imputed datasets. In pursuit of our primary objective, which is accurate diagnosis, we employed diagnostic accuracy (classification accuracy) as a pivotal metric to assess both imputation and feature selection techniques. This assessment encompassed seven classifiers (logistic regression (LR) classifier, random forest (RF) classifier, support machine classifier (SVC), extreme gradient boosting (XGBoost) , LASSO classifier, SCAD classifier and Elastic Net classifier)enhancing the comprehensiveness of our evaluation.ResultsThe proposed framework imputation-variable selection-prediction is quite suitable to the collected vaginal prolapse datasets. It is observed that the original dataset is well imputed by GAIN first, and then 9 most significant features were selected using BAR from the original 67 features in GAIN imputed dataset, with only negligible loss in model prediction. BAR is superior to the other two variable selection methods in our tests.ConcludesOverall, combining the imputation, classification and variable selection, we achieve good interpretability while maintaining high accuracy in computer-aided medical diagnosis.

Data Analytics Enables Reconstruction of Missing Segments in Well Data History

_ This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 208137, “Reconstruction of Missing Segments in Well Data History Using Data Analytics,” by Yuanjun Li, SPE, and Roland Horne, SPE, Stanford University, and Ahmed Alshmakhy, SPE, ADNOC, et al. The paper has not been peer reviewed. _ The problem of missing data is a common one in well-production records. An incomplete data set is commonly simplified by omitting all observations with missing values, which can lead to significant information loss. In the complete paper, the authors developed an efficient procedure that enables fast reconstruction of the entire production data set with multiple missing sections in different variables. Ultimately, the complete information can support the reservoir history-matching process and production allocation and can develop models for reservoir performance prediction. Introduction Missing values are a prevalent issue in pressure transient analysis. Their occurrence is often unavoidable and is a serious problem because many data-mining algorithms cannot work with data sets that are missing values. Sources of missing field data include operational problems, changes in operating conditions, and variable sampling frequencies. Imputation Technique of Missing Values Case Deletion. If a missing data problem exists in a multivariate series, the time series may not be evaluated appropriately. In time-series data, each record is unique. Thus, deleting a record would result in a series with gaps, which would be unusable for many analyses. Furthermore, the time-series plot would be truncated and riddled with flaws. Thus, to conduct a time-series analysis, those missing observations must be estimated and reinserted. When the number of missing values in the data set is considerable, eliminating the associated cases might result in the loss of vital and valuable information. Researchers have found that case elimination is not appropriate when the missing rate is greater than 3%. Physics-Based Imputation. Any physics-driven diagnostic or model is based on fundamental assumptions that may or may not be correct in all situations but are intended to represent reservoir behavior. While data-driven models do not make any assumptions about underlying physics, they rely on training data. Therefore, data analytics and machine learning can be an ideal substitute. Local Statistical Approach. Local statistical methods include the K-nearest-neighbor (KNN) imputation and local-least-square imputation, two of the most well-known algorithms. To impute the incomplete data, the KNN imputation employs a pairwise correlation between the target gene with missing values and the K closest reference genes. For missing-value imputation, the techniques in this local method use local similarity patterns in the data sets or a specific column of data.

Adverse maternal outcomes associated with major fetal malformations after singleton live birth

Major fetal malformations complicate 2% to 5% of live births. It is unclear what effect fetal malformations have on severe maternal morbidity. This study aimed to compare maternal outcomes between individuals with a fetus with major or minor fetal malformations and those with a fetus without major or minor fetal malformations. This was a secondary analysis of the Consortium on Safe Labor database. Our study was limited to the current analysis of pregnant individuals with a singleton live birth. Major fetal malformations based on the Centers for Disease Control and Prevention's criteria were defined. Fetal malformations that did not meet the criteria for major fetal malformations were categorized as minor fetal malformations. Our primary maternal outcome was severe maternal morbidity as defined by the Centers for Disease Control and Prevention. Missing values were imputed by multiple imputation using the k-nearest neighbor imputation method. Poisson regression with robust error variance was used to obtain adjusted relative risks with 95% confidence intervals, controlling for confounders. Of 216,881 deliveries, there were 201,860 cases (93.1%) with no congenital malformation, 12,106 cases (5.6%) with minor fetal malformations, and 2845 cases (1.3%) with major fetal malformations. Compared with individuals with no fetal malformation, those with major fetal malformations were more likely to have severe maternal morbidity (0.7% vs 1.2%; adjusted relative risk, 1.51; 95% confidence interval, 1.07-2.12), postpartum hemorrhage (3.6% vs 6.9%; adjusted relative risk, 1.76; 95% confidence interval, 1.50-2.06), preeclampsia (5.1% vs 8.3%; adjusted relative risk, 1.48; 95% confidence interval, 1.31-1.67), and cesarean delivery (26.7% vs 42.3%; adjusted relative risk, 1.51; 95% confidence interval, 1.45-1.58). Compared with individuals with no fetal malformation, those with minor fetal malformations were more likely to have severe maternal morbidity (0.7% vs 1.4%; adjusted relative risk, 1.73; 95% confidence interval, 1.48-2.02), maternal death (0.01% vs 0.03%; adjusted relative risk, 4.50; 95% confidence interval, 1.18-17.19), postpartum hemorrhage (3.6% vs 6.1%; adjusted relative risk, 1.54; 95% confidence interval, 1.41-1.68), preeclampsia (5.1% vs 8.6%; adjusted relative risk, 1.50; 95% confidence interval, 1.41-1.60), superimposed preeclampsia (1.2% vs 2.4%; adjusted relative risk, 1.25; 95% confidence interval, 1.14-1.38), cesarean delivery (26.7% vs 39.6%; adjusted relative risk, 1.38; 95% confidence interval, 1.35-1.41), chorioamnionitis (3.0% vs 4.7%; adjusted relative risk, 1.41; 95% confidence interval, 1.29-1.53), and postpartum endometritis (0.6% vs 1.0%; adjusted relative risk, 1.58; 95% confidence interval, 1.31-1.90). Major and minor congenital fetal malformations are independent risk factors for severe maternal morbidity and other pregnancy complications.

ABTCN: an efficient hybrid deep learning approach for atmospheric temperature prediction.

Temperature prediction is an important and significant step for monitoring global warming and the environment to save and protect human lives. The climatology parameters such as temperature, pressure, and wind speed are time-series data and are well predicted with data driven models. However, data-driven models have certain constraints, due to which these models are unable to predict the missing values and erroneous data caused by factors like sensor failure and natural disasters. In order to solve this issue, an efficient hybrid model, i.e., attention-based bidirectional long short term memory temporal convolution network (ABTCN) architecture is proposed. ABTCN uses k-nearest neighbor (KNN) imputation method for handling the missing data. A bidirectional long short term memory (Bi-LSTM) network with self-attention mechanism and temporal convolutional network (TCN) model that aids in the extraction of features from complex data and prediction of long data sequence. The performance of the proposed model is evaluated in comparison to various state-of-the-art deep learning models using error metrics such as MAE, MSE, RMSE, and R2 score. It is observed that our proposed model is superior over other models with high accuracy.

A machine-learning approach for prediction of hospital mortality in cancer-related sepsis

ObjectiveTo develop a machine learning model to predict hospital mortality and identify risk factors in cancer-related sepsis patients. MethodWe obtained data from the Medical Information Mart for Intensive Care (MIMIC)-IV critical care data set, which included patients who diagnosed with cancer and fulfilled the definition of sepsis between 2008 and 2019. The data set was randomly split into a training set and a validation set. The dataset was imputed using the K-Nearest Neighbor (KNN) imputation model. An advanced machine learning model called CatBoost was established and then assessed by SHAP value. ResultsA total of 5081 patients were included in the final analysis. The cancer-related sepsis patients had a lower hospital survival (13.8% vs. 25.3%, P < 0.001) than non-cancer-related patients.For cancer-related sepsis patients, ensemble learning algorithms were superior to others with better accuracy and larger AUC, such as CatBoost (AUC: 0.828), LightGBM (AUC: 0.818), and Random Forest Classifier (AUC: 0.803). An evaluation of the performance suggested that the CatBoost model with the most powerful discrimination to predict hospital mortality, outperformed other models with a sensitivity of 76% and a specificity of 74%. The best cutoff was 0.223 for the CatBoost model. In addition, CatBoost also outperformed other severity scores such as SAPS-II (AUC: 0.725) and SOFA (AUC: 0.682). Urine output and the minimum BUN level on admission were the most important features for the hospital mortality prediction of cancer-related sepsis, while the patients’ age and the urine output on admission for non-cancer-related patients. ConclusionFor cancer-related sepsis patients, CatBoost model was a better prediction model. It was easy for clinicians to access by use of common clinical vital signs or laboratory examination parameters, which provides convenience for them to evaluate patient’s condition and make follow-up treatments.

The Performance Evaluation of The Random Forest Algorithm for A Gene Selection in Identifying Genes Associated with Resectable Pancreatic Cancer in Microarray Dataset: A Retrospective Study.

In microarray datasets, hundreds and thousands of genes are measured in a small number of samples, and sometimes due to problems that occur during the experiment, the expression value of some genes is recorded as missing. It is a difficult task to determine the genes that cause disease or cancer from a large number of genes. This study aimed to find effective genes in pancreatic cancer (PC). First, the K-nearest neighbor (KNN) imputation method was used to solve the problem of missing values (MVs) of gene expression. Then, the random forest algorithm was used to identify the genes associated with PC. In this retrospective study, 24 samples from the GSE14245 dataset were examined. Twelve samples were from patients with PC, and 12 samples were from healthy control. After preprocessing and applying the fold-change technique, 29482 genes were used. We used the KNN imputation method to impute when a particular gene had MVs. Then, the genes most strongly associated with PC were selected using the random forest algorithm. We classified the dataset using support vector machine (SVM) and naïve bayes (NB) classifiers, and F-score and Jaccard indices were reported. Out of the 29482 genes, 1185 genes with fold-changes greater than 3 were selected. After selecting the most associated genes, 21 genes with the most important value were identified. S100P and GPX3 had the highest and lowest importance values, respectively. The F-score and Jaccard value of the SVM and NB classifiers were 95.5, 93, 92, and 92 percent, respectively. This study is based on the application of the fold change technique, imputation method, and random forest algorithm and could find the most associated genes that were not identified in many studies. We therefore suggest researchers use the random forest algorithm to detect the related genes within the disease of interest.

Analisis Kinerja Algoritma K-Nearest Neighbor Imputation (KNNI) Untuk Missing Value Pada Klasifikasi Data Mining

At this time many researchers use datasets for research, in the dataset there is a lot of important information provided to make it easier for researchers to process the data, but there are obstacles when trying to process the data, namely some data values are lost, or even damaged (incompatible) with other values. The number of attributes and data samples in the dataset is unlimited which makes it difficult for researchers to find important information based on the goals of each researcher. The missing or damaged values are known as missing values and this often happens when researchers process data taken from datasets. When researchers classify a dataset, of course it is difficult to classify because if there is missing data, the classification process cannot be carried out completely. Of course this will greatly affect the results obtained and also the results of the accuracy of the process. The problem of missing data (missing value) must be resolved, one way to solve this problem is by using the K-Nearest Neighbor Imputation (K-NNI) algorithm. The KNNI algorithm is one of the data mining algorithms that can restore the missing value problem by performing pattern recognition of the nearest data from the missing data. The final result after applying KNNI in fixing the missing value is to obtain a weight value for each K closest observation of 51.6161 on K5. Datasets that experience missing values have been adjusted based on the weight values and the implementation of the KNNI algorithm with the rapidminer application. By returning data using the KNNI algorithm, it is hoped that it can help solve these problems so that the results obtained from the classification process are more accurate and the results of greater accuracy

A clinical decision support system for heart disease prediction with ensemble two-fold classification framework

Cardiovascular disease (CVD) is a severe public health concern globally. Early and accurate CVD diagnosis is a difficult task but a necessary endeavour required to prevent further damage and protect patients’ lives. Machine Learning (ML)-based Clinical Decision Support Systems (CDSS) have the potential to assist healthcare providers in making accurate CVD diagnoses and treatments. Clinical data usually contains missing values (MVs); hence, the incorporated imputation techniques for ML have become a critical consideration when working with real-world medical datasets. Furthermore, removing instances with MVs will lead to essential data loss and produce incorrect results. To overcome these issues, this paper proposes an efficient and reliable CDSS with Ensemble Two-Fold Classification (ETC) framework for classifying heart diseases. The effectiveness of the proposed ETC framework using different supervised ML algorithms is evaluated with four distinct imputation methods for handling MVs over the standard benchmark dataset, viz., the University of California, Irwin (UCI). Experimental results show that our proposed ETC framework with the k-Nearest Neighbors(k-NN) imputation method achieves better classification accuracy of 0.9999 and a lesser error rate of 0.0989 compared to other imputation methods and classifiers with similar execution times.

Optimized fuzzy clustering‐based k‐nearest neighbors imputation for mixed missing data in software development effort estimation

AbstractContextSoftware development effort estimation (SDEE) is one of the most challenging aspects in project management. The presence of missing data (MD) in software attributes makes SDEE even more complex. K‐nearest neighbors imputation (KNNI) has been widely used in SDEE to deal with the MD issue. However, KNNI, in its classical process, has low tolerance to imprecision and uncertainty especially when dealing with categorical features. When dealing with categorical attributes, KNNI uses a classical approach, employing mainly numbers or classical intervals to represent software attributes and similarity measures originally designed for numerical attributes.ObjectivesThis paper evaluates the use of an optimized fuzzy clustering‐based KNNI (FC‐KNNI) and compares it with classical KNN when dealing with mixed data in the context of SDEE.MethodsWe investigate the effect of two imputation techniques (FC‐KNNI and KNNI) on five SDEE techniques: case‐based reasoning, fuzzy case‐based reasoning, support vector regression, multilayer perceptron, and reduced‐error pruning tree. The evaluation is carried out using six publicly available datasets for SDEE using two performance measures, standardized accuracy (SA), and Pred (0.25). The Wilcoxon statistical test is also performed to assess the significance of results.ResultsThe results are promising in the sense that using an imputation technique designed for mixed data is better than reusing methods originally designed for numerical data. We found that FC‐KNNI significantly outperforms KNNI regardless of the SDEE technique and dataset used. Another important finding is that F‐CBR improved the analogy process compared to CBR.ConclusionThe introduction of fuzzy sets and fuzzy clustering in the analogy process improves its performances in terms of SA and Pred (0.25).

A comparison of imputation methods for categorical data

ObjectivesMissing data is commonplace in clinical databases, which are being increasingly used for research. Without giving any regard to missing data, results from analysis may become biased and unrepresentative. Clinical databases contain mainly categorical variables. This study aims to assess the methods used for imputation in categorical variables. Materials and methodsWe utilized data extracted from paper-based maternal health records from Kawempe National Referral Hospital, Uganda. We compared the following imputation methods for categorical data in an empirical analysis: Mode, K-Nearest Neighbors (KNN), Random Forest (RF), Sequential Hot-Deck (SHD), and Multiple Imputation by Chained Equations (MICE). The five imputation methods were first compared by accuracy of predicting the missing values. Next, the imputation methods were compared by predictive accuracy of the outcome variable in four classifiers. The consistency of performance of imputation methods across different levels of missing data (5%–50 %) was assessed by Kendall's W test. ResultsKNN imputation had the highest precision score at levels (5%–50 %) of MCAR missing data. At lower proportions of missing data (5 %, 10 %, 15 %, 20 %), RF imputation had the second-highest precision score. SHD imputation had the worst precision at all levels of missing data. In the prediction of the outcome, the methods performed differently at all proportions of missing data in the four classifiers. Even though KNN imputation was the best method in predicting the missing values, it did not consistently enhance the predictive accuracy of the classifiers at all levels of missing data. Our findings show that a high precision score of an imputation method does not translate into higher predictive accuracy in classifiers. ConclusionsKNN imputation is the best method in predicting missing values in categorical variables. There is no universal best imputation method that yields the highest predictive accuracy at all proportions of missing data.

An effective ensemble method for missing data imputation

The presence of missing data in a dataset plays a vital role in the design of classification, clustering, or regression methods. An efficient missing data imputation can enhance the overall performance of a machine learning method. This paper ensembles k-nearest neighbour imputation, local least square imputation, miss forest imputation, and k-means clustering imputation using the bagging approach to handle missing values over a wide range of datasets. The method has been tested with eight different datasets in terms of root mean square error, median absolute percentage error, mean absolute percentage error, and standard deviation. Experimental results show that our method gives a low error rate compared to its closed competitors.

Joint wind and ice hazard for transmission lines in mountainous terrain

This paper focuses on analyzing the ice and wind conditions for a high voltage transmission line system going over mountainous terrain in British Columbia, Canada. After analyzing sixteen surrounding weather stations, an Artificial Neural Network (ANN) is performed for wind speed predictions, the Inverse Distance Weighted Interpolation method (IDW) is used for temperature estimations, and the K Nearest Neighbor Imputation (KNNI) is performed for precipitation rate on the unsampled site of interest. In-cloud icing is estimated using a simple empirical equation proposed by Makkonen & Ahti (1995), in addition to the ice accretion model proposed by Makkonen (2000). The weather data from the described procedure is compared with data from a Weather Research and Forecast model (WRF) reported in previous studies. The wind speed and ice accretion data are fitted to the Weibull and Generalized Pareto distributions, respectively, and five hundred years of data pairs are simulated in addition to the historical data based on those distributions. Finally, hazard contours are drawn, and the results are compared with the combined wind and ice values proposed in the National Standard of Canada (CSA-C22.3) for different reliability levels.