The Feature Selection Effect on Missing Value Imputation of Medical Datasets

Liu, Chia Hui; Tsai, Chih‐Fong; Sue, Kuen Liang; Huang, Min

doi:10.3390/app10072344

Cited by 32 publications

(18 citation statements)

References 41 publications

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“…RF is one of the excellent choices; it is tolerable in data noise and trains with reasonable accuracy. The decision trees are independent of each other [20]. The tree rules can be generated using two techniques.…”

Section: Random Forest Algorithmmentioning

confidence: 99%

Automatic missing value imputation for cleaning phase of diabetic’s readmission prediction model

Hoque

Hossen

Sayeed

et al. 2022

IJECE

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Recently, the industry of healthcare started generating a large volume of datasets. If hospitals can employ the data, they could easily predict the outcomes and provide better treatments at early stages with low cost. Here, data analytics (DA) was used to make correct decisions through proper analysis and prediction. However, inappropriate data may lead to flawed analysis and thus yield unacceptable conclusions. Hence, transforming the improper data from the entire data set into useful data is essential. Machine learning (ML) technique was used to overcome the issues due to incomplete data. A new architecture, automatic missing value imputation (AMVI) was developed to predict missing values in the dataset, including data sampling and feature selection. Four prediction models (i.e., logistic regression, support vector machine (SVM), AdaBoost, and random forest algorithms) were selected from the well-known classification. The complete AMVI architecture performance was evaluated using a structured data set obtained from the UCI repository. Accuracy of around 90% was achieved. It was also confirmed from cross-validation that the trained ML model is suitable and not over-fitted. This trained model is developed based on the dataset, which is not dependent on a specific environment. It will train and obtain the outperformed model depending on the data available.

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Section: Random Forest Algorithmmentioning

confidence: 99%

Automatic missing value imputation for cleaning phase of diabetic’s readmission prediction model

Hoque

Hossen

Sayeed

et al. 2022

IJECE

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show abstract

“…The percentage of missing data was calculated and variables with more than 30% missing data were rejected from the analysis since data imputation for larger amounts of missingness may become very imprecise [52]. Using MATLAB version 2019b (The MathWorks, Inc., Natick, MA, USA), missing data for the remaining variables were imputed by the k-nearest neighbors technique using k = 3 [53]. Each ∆ variable was normalized with respect to its absolute maximum value.…”

Section: Statistical Analysesmentioning

confidence: 99%

“…Considering HDLSS data, reducing the number of features is crucial to perform nonoverparameterized classification-based analyses [54]. Feature selection techniques can be classified into filter, wrapper, and embedded methods [53]. Filter methods can be combined with any machine learning model and are much faster and less prone to overfitting than wrapper and embedded methods [55].…”

Section: Feature Selectionmentioning

confidence: 99%

Identification of Statin’s Action in a Small Cohort of Patients with Major Depression

et al. 2021

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Statins are widely used as an effective therapy for ischemic vascular disorders and employed for primary and secondary prevention in cardiac and cerebrovascular diseases. Their hemostatic mechanism has also been shown to induce changes in cerebral blood flow that may result in neurocognitive improvement in subjects with Major Depressive Disorder. Behavioral data, various blood tests, and resting-state brain perfusion data were obtained at the start of this study and three months post-therapy from a small cohort of participants diagnosed with Major Depressive Disorder. Subjects received either rosuvastatin (10 mg) or placebo with their standard selective serotonin reuptake inhibitors therapy. At the end of the study, patients using rosuvastatin reported more positive mood changes than placebo users. However, standard statistical tests revealed no significant differences in any non-behavioral variables before and after the study. In contrast, feature selection techniques allowed identifying a small set of variables that may be affected by statin use and contribute to mood improvement. Classification models built to assess the distinguishability between the two groups showed an accuracy higher than 85% using only five selected features: two peripheral platelet activation markers, perfusion abnormality in the left inferior temporal gyrus, Attention Switching Task Reaction latency, and serum phosphorus levels. Thus, using machine learning tools, we could identify factors that may be causing self-reported mood improvement in patients due to statin use, possibly suggesting a regulatory role of statins in the pathogenesis of clinical depression.

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“…The simplest one is arguably to substitute the missing values with their ensemble average [7]. More sophisticated imputation strategies obtain better results employing, e.g., multilayer perceptrons, extreme gradient boosting machines, and support vector machines [8][9][10]. For example, Vivar et al [11] improved the classification of individuals in the datasets of the Alzheimer's Disease Neuroimaging Initiative (ADNI) and the Parkinson's Progression Markers Initiative (PPMI) by employing a multiple recurrent graph convolutional network to impute the missing features (the brain volumes obtained from magnetic resonance imaging (MRI)).…”

Section: Introductionmentioning

confidence: 99%

Neural Network Training with Highly Incomplete Datasets

Chang,

Natali,

Jamialahmadi

et al. 2021

Preprint

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Neural network training and validation rely on the availability of large high-quality datasets. However, in many cases only incomplete datasets are available, particularly in health care applications, where each patient typically undergoes different clinical procedures or can drop out of a study. Since the data to train the neural networks need to be complete, most studies discard the incomplete datapoints, which reduces the size of the training data, or impute the missing features, which can lead to artefacts. Alas, both approaches are inadequate when a large portion of the data is missing. Here, we introduce GapNet, an alternative deep-learning training approach that can use highly incomplete datasets. First, the dataset is split into subsets of samples containing all values for a certain cluster of features. Then, these subsets are used to train individual neural networks. Finally, this ensemble of neural networks is combined into a single neural network whose training is fine-tuned using all complete datapoints. Using two highly incomplete real-world medical datasets, we show that GapNet improves the identification of patients with underlying Alzheimer's disease pathology and of patients at risk of hospitalization due to Covid-19. By distilling the information available in incomplete datasets without having to reduce their size or to impute missing values, GapNet will permit to extract valuable information from a wide range of datasets, benefiting diverse fields from medicine to engineering.

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The Feature Selection Effect on Missing Value Imputation of Medical Datasets

Cited by 32 publications

References 41 publications

Automatic missing value imputation for cleaning phase of diabetic’s readmission prediction model

Automatic missing value imputation for cleaning phase of diabetic’s readmission prediction model

Identification of Statin’s Action in a Small Cohort of Patients with Major Depression

Neural Network Training with Highly Incomplete Datasets

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