Performance Analysis of Various Missing Value Imputation Methods on Heart Failure Dataset

Khaldy, Mohammad Al; Kambhampati, Chandrasekhar

doi:10.1007/978-3-319-56991-8_31

Cited by 8 publications

(5 citation statements)

References 29 publications

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“…The dataset under consideration is a real-life heart failure dataset [29]. In this dataset, there are 60 features for 1944 patient records (See Table 3 in Appendix A).…”

Section: Selecting Features For a Clinical Datasetmentioning

confidence: 99%

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Addressing Optimisation Challenges for Datasets with Many Variables, Using Genetic Algorithms to Implement Feature Selection

Gordon¹,

Kambhampati²,

Alabad³

2022

ACRT

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This article provides an optimisation method using a Genetic Algorithm approach to apply feature selection techniques for large data sets to improve accuracy. This is achieved through improved classification, a reduced number of features, and furthermore it aids in interpreting the model. A clinical dataset, based on heart failure, is used to illustrate the nature of the problem and to show the effectiveness of the techniques developed. Clinical datasets are sometimes characterised as having many variables. For instance, blood biochemistry data has more than 60 variables that have led to complexities in developing predictions of outcomes using machine-learning and other algorithms. Hence, techniques to make them more tractable are required. Genetic Algorithms can provide an efficient and low numerically complex method for effectively selecting features. In this paper, a way to estimate the number of required variables is presented, and a genetic algorithm is used in a “wrapper” form to select features for a case study of heart failure data. Additionally, different initial populations and termination conditions are used to arrive at a set of optimal features, and these are then compared with the features obtained using traditional methodologies. The paper provides a framework for estimating the number of variables and generations required for a suitable solution.

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“…The dataset under consideration is a real-life heart failure dataset [29]. In this dataset, there are 60 features for 1944 patient records (See Table 3 in Appendix A).…”

Section: Selecting Features For a Clinical Datasetmentioning

confidence: 99%

“…Vector Machine (SVM). The different datasets obtained were tested in order to select a good set for feature selection [29]. The selection of a dataset was based on accuracy, sensitivity and specificity.…”

Section: Selecting Features For a Clinical Datasetmentioning

confidence: 99%

Addressing Optimisation Challenges for Datasets with Many Variables, Using Genetic Algorithms to Implement Feature Selection

Gordon¹,

Kambhampati²,

Alabad³

2022

ACRT

View full text Add to dashboard Cite

show abstract

“…The data sets were imputed by different methods such as Concept Most Common Imputation (CMCI) and Support Victor Machine (SVM). Different classification methods have been applied to these datasets to select which dataset will be trained [18]. The performance of these datasets was measured using accuracy, sensitivity, and specificity.…”

Section: Genetic Algorithms (Gas) Experimentsmentioning

confidence: 99%

Genetic Algorithms as a Feature Selection Tool in Heart Failure Disease

Alabed

Kambhampati

Gordon

2020

Advances in Intelligent Systems and Computing

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A great wealth of information is hidden in clinical datasets, which could be analyzed to support decision-making processes or to better diagnose patients. Feature selection is one of the data pre-processing that selects a set of input features by removing unneeded or irrelevant features. Various algorithms have been used in healthcare to solve such problems involving complex medical data. This paper demonstrates how Genetic Algorithms offer a natural way to solve feature selection amongst data sets, where the fittest individual choice of variables is preserved over different generations. In this paper, a Genetic Algorithms is introduced as a feature selection method and shown to be effective in aiding understanding of such data.

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“…Random forest (RF) can process high-dimensional data with high accuracy, while it has a large computational cost when processing a large amount of data [6]. Support vector machine (SVM) is insensitive to outliers and has high robustness, but this algorithm is of high computational complexity [7]. Neural network has excellent performance, nevertheless, it requires a huge training data set and is prone to over-fitting [8].…”

Section: Introductionmentioning

confidence: 99%

Missing Data Imputation Approach Based on Adaptive Compressed Sensing

2022

Proceedings of 2022 the 12th International Workshop on Computer Science and Engineering

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The paper presents a missing data imputation method based on compressed Sensing (CS). First of all, the problem of data imputation is translated into the recovery of sparse vector under the framework of compressed sensing. Secondly, we propose an improved greedy reconstruction algorithm called Double Try Sparsity Adaptive Matching Pursuit (DTSAMP). The algorithm obtains the estimation of sparsity by trying twice to approximate the value of sparsity, and then approximates the estimated value in each iteration. As a result, the missing data sets can be reconstructed without prior information of the sparsity. Furthermore, the step size and support set are well controlled by setting thresholds during the iteration. The simulation results show that the proposed algorithm is superior to other methods in terms of reconstruction speed and accuracy, as well as better robustness.

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Performance Analysis of Various Missing Value Imputation Methods on Heart Failure Dataset

Cited by 8 publications

References 29 publications

Addressing Optimisation Challenges for Datasets with Many Variables, Using Genetic Algorithms to Implement Feature Selection

Addressing Optimisation Challenges for Datasets with Many Variables, Using Genetic Algorithms to Implement Feature Selection

Genetic Algorithms as a Feature Selection Tool in Heart Failure Disease

Missing Data Imputation Approach Based on Adaptive Compressed Sensing

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