High-Dimensional LASSO-Based Computational Regression Models: Regularization, Shrinkage, and Selection

Emmert‐Streib, Frank; Dehmer, Matthias

doi:10.3390/make1010021

Cited by 110 publications

(70 citation statements)

References 74 publications

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“…Regarding the disadvantages of LASSO, it can select a limited number of features and often only one feature per group of features. In addition, for low-dimensional cases, model interpretability is low [52][53][54]. Finally, this study only focused on in-hospital mortality, and future studies will examine the nomogram over the long term.…”

Section: Discussionmentioning

confidence: 99%

Establishment and validation of a risk model for prediction of in-hospital mortality in patients with acute ST-elevation myocardial infarction after primary PCI

Gao

Dang

et al. 2020

BMC Cardiovasc Disord

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Background Currently, how to accurately determine the patient prognosis after a percutaneous coronary intervention (PCI) remains unclear and may vary among populations, hospitals, and datasets. The aim of this study was to establish a prediction model of in-hospital mortality risk after primary PCI in patients with acute ST-elevated myocardial infarction (STEMI). Methods This was a multicenter, observational study of patients with acute STEMI who underwent primary PCI. The outcome was in-hospital mortality. The least absolute shrinkage and selection operator (LASSO) method was used to select the features that were the most significantly associated with the outcome. A regression model was built using the selected variables to select the significant predictors of mortality. Receiver operating characteristic (ROC) curve and decision curve analysis (DCA) were used to evaluate the performance of the nomogram. Results Totally, 1169 and 316 patients were enrolled in the training and validation sets, respectively. Fourteen predictors were identified by the LASSO analysis: sex, Killip classification, left main coronary artery disease (LMCAD), grading of thrombus, TIMI classification, slow flow, application of IABP, administration of β-blocker, ACEI/ARB, symptom-to-door time (SDT), symptom-to-balloon time (SBT), syntax score, left ventricular ejection fraction (LVEF), and CK-MB peak. The mortality risk prediction nomogram achieved good discrimination for in-hospital mortality (training set: C-statistic = 0.987; model calibration: P = 0.722; validation set: C-statistic = 0.984, model calibration: P = 0.669). Area under the curve (AUC) values for the training and validation sets are 0.987 (95% CI: 0.981–0.994, P = 0.003) and 0.990 (95% CI: 0.987–0.998, P = 0.007), respectively. DCA shows that the nomogram can achieve good net benefit. Conclusions A novel nomogram was developed and is a simple and accurate tool for predicting the risk of in-hospital mortality in patients with acute STEMI who underwent primary PCI.

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Section: Discussionmentioning

confidence: 99%

Establishment and validation of a risk model for prediction of in-hospital mortality in patients with acute ST-elevation myocardial infarction after primary PCI

Gao

Dang

et al. 2020

BMC Cardiovasc Disord

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“…Additionally, the relationship between the selected variables and e-cigarette use may not be adequately explained by a multivariable logistic regression model. Other limitations of the ML algorithms include the fact that Boruta is computationally expensive, especially for large datasets, and LASSO has no grouping property, and as such, tends to select only one variable from a group of highly correlated variables [ 54 , 81 ].…”

Section: Discussionmentioning

confidence: 99%

Factors Associated with E-Cigarette Use in U.S. Young Adult Never Smokers of Conventional Cigarettes: A Machine Learning Approach

Atuegwu

Oncken

Laubenbacher

et al. 2020

IJERPH

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E-cigarette use is increasing among young adult never smokers of conventional cigarettes, but the awareness of the factors associated with e-cigarette use in this population is limited. The goal of this work was to use machine learning (ML) algorithms to determine the factors associated with current e-cigarette use among US young adult never cigarette smokers. Young adult (18–34 years) never cigarette smokers from the 2016 and 2017 Behavioral Risk Factor Surveillance System (BRFSS) who reported current or never e-cigarette use were used for the analysis (n = 79,539). Variables associated with current e-cigarette use were selected by two ML algorithms (Boruta and Least absolute shrinkage and selection operator (LASSO)). Odds ratios were calculated to determine the association between e-cigarette use and the variables selected by the ML algorithms, after adjusting for age, gender and race/ethnicity and incorporating the BRFSS complex design. The prevalence of e-cigarette use varied across states. Factors previously reported in the literature, such as age, race/ethnicity, alcohol use, depression, as well as novel factors associated with e-cigarette use, such as disabilities, obesity, history of diabetes and history of arthritis were identified. These results can be used to generate further hypotheses for research, increase public awareness and help provide targeted e-cigarette education.

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“…Overall, the advantage of a ridge regression and general regularized regression model is that regularization can reduce the variance by increasing the bias. Interestingly, this can improve the prediction accuracy of a model [19].…”

Section: Regularization: Ridge Regressionmentioning

confidence: 99%

“…This definition is based on SSR and SST in Equations (17) and (19). The COD is a measure of how well the model explains the variance of the response variables.…”

Section: R 2 and Adjusted Rmentioning

confidence: 99%

“…For the analysis of supervised learning models, such as regression or classification methods [15][16][17][18][19][20], allowing to estimate a prediction error model selection and model assessment are key concepts for finding the best model for a given data set. Interestingly, regarding the definition of a best model, there are two complementary approaches with a different underlying philosophy [21,22].…”

Section: Introductionmentioning

confidence: 99%

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Evaluation of Regression Models: Model Assessment, Model Selection and Generalization Error

Emmert‐Streib

Dehmer

2019

MAKE

Self Cite

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When performing a regression or classification analysis, one needs to specify a statistical model. This model should avoid the overfitting and underfitting of data, and achieve a low generalization error that characterizes its prediction performance. In order to identify such a model, one needs to decide which model to select from candidate model families based on performance evaluations. In this paper, we review the theoretical framework of model selection and model assessment, including error-complexity curves, the bias-variance tradeoff, and learning curves for evaluating statistical models. We discuss criterion-based, step-wise selection procedures and resampling methods for model selection, whereas cross-validation provides the most simple and generic means for computationally estimating all required entities. To make the theoretical concepts transparent, we present worked examples for linear regression models. However, our conceptual presentation is extensible to more general models, as well as classification problems.

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High-Dimensional LASSO-Based Computational Regression Models: Regularization, Shrinkage, and Selection

Cited by 110 publications

References 74 publications

Establishment and validation of a risk model for prediction of in-hospital mortality in patients with acute ST-elevation myocardial infarction after primary PCI

Establishment and validation of a risk model for prediction of in-hospital mortality in patients with acute ST-elevation myocardial infarction after primary PCI

Factors Associated with E-Cigarette Use in U.S. Young Adult Never Smokers of Conventional Cigarettes: A Machine Learning Approach

Evaluation of Regression Models: Model Assessment, Model Selection and Generalization Error

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