Using recursive feature elimination in random forest to account for correlated variables in high dimensional data

Darst, Burcu F.; Malecki, Kristen; Engelman, Corinne D.

doi:10.1186/s12863-018-0633-8

Cited by 256 publications

(117 citation statements)

References 16 publications

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“…To select the optimal subset of clinical features, an RFE processed with 10-fold cross-validation was used to select the best subset. The RFE could eliminate the redundant and irrelative information from the COVID-19 patients and enhance the performance of the RF classification model ( Darst, Malecki & Engelman, 2018 ). The results selected 11 clinical characteristics, Myo, CD8, age, LDH, LMR, CD45, Th/Ts, dyspnea, NLR, D-Dimer and CK with the highest accuracy at 0.9905 ( Fig.…”

Section: Resultsmentioning

confidence: 99%

A descriptive study of random forest algorithm for predicting COVID-19 patients outcome

Wang

Yu²,

Hua

et al. 2020

PeerJ

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Background The outbreak of coronavirus disease 2019 (COVID-19) that occurred in Wuhan, China, has become a global public health threat. It is necessary to identify indicators that can be used as optimal predictors for clinical outcomes of COVID-19 patients. Methods The clinical information from 126 patients diagnosed with COVID-19 were collected from Wuhan Fourth Hospital. Specific clinical characteristics, laboratory findings, treatments and clinical outcomes were analyzed from patients hospitalized for treatment from 1 February to 15 March 2020, and subsequently died or were discharged. A random forest (RF) algorithm was used to predict the prognoses of COVID-19 patients and identify the optimal diagnostic predictors for patients’ clinical prognoses. Results Seven of the 126 patients were excluded for losing endpoints, 103 of the remaining 119 patients were discharged (alive) and 16 died in the hospital. A synthetic minority over-sampling technique (SMOTE) was used to correct the imbalanced distribution of clinical patients. Recursive feature elimination (RFE) was used to select the optimal subset for analysis. Eleven clinical parameters, Myo, CD8, age, LDH, LMR, CD45, Th/Ts, dyspnea, NLR, D-Dimer and CK were chosen with AUC approximately 0.9905. The RF algorithm was built to predict the prognoses of COVID-19 patients based on the best subset, and the area under the ROC curve (AUC) of the test data was 100%. Moreover, two optimal clinical risk predictors, lactate dehydrogenase (LDH) and Myoglobin (Myo), were selected based on the Gini index. The univariable logistic analysis revealed a substantial increase in the risk for in-hospital mortality when Myo was higher than 80 ng/ml (OR = 7.54, 95% CI [3.42–16.63]) and LDH was higher than 500 U/L (OR = 4.90, 95% CI [2.13–11.25]). Conclusion We applied an RF algorithm to predict the mortality of COVID-19 patients with high accuracy and identified LDH higher than 500 U/L and Myo higher than 80 ng/ml to be potential risk factors for the prognoses of COVID-19 patients in the early stage of the disease.

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Section: Resultsmentioning

confidence: 99%

A descriptive study of random forest algorithm for predicting COVID-19 patients outcome

Wang

Yu²,

Hua

et al. 2020

PeerJ

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“…Recursive feature elimination (RFE) is a method that follows an iterative procedure where features are ranked based upon their importance in classifying the training set and then the feature-(s) with smallest ranking criterion is(are) removed. The RFE was implemented from Support Vector Machines 28 or Random Forest 29 algorithms. Besides that, both algorithms can be used to rank the feature importance and also remove correlated predictors.…”

Section: (Ii) Wrapper Methodsmentioning

confidence: 99%

Methods for predicting vaccine immunogenicity and reactogenicity

Gonzalez-Dias

Lee

Sorgi

et al. 2019

Human Vaccines & Immunotherapeutics

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Subjects receiving the same vaccine often show different levels of immune responses and some may even present adverse side effects to the vaccine. Systems vaccinology can combine omics data and machine learning techniques to obtain highly predictive signatures of vaccine immunogenicity and reactogenicity. Currently, several machine learning methods are already available to researchers with no background in bioinformatics. Here we described the four main steps to discover markers of vaccine immunogenicity and reactogenicity: (1) Preparing the data; (2) Selecting the vaccinees and relevant genes; (3) Choosing the algorithm; (4) Blind testing your model. With the increasing number of Systems Vaccinology datasets being generated, we expect that the accuracy and robustness of signatures of vaccine reactogenicity and immunogenicity will significantly improve.

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“…The Recursive Feature Elimination combined with Random Forest (RFE-RF) classification has been reported as a promising technique to effectively handling the fusion of diverse data sources, such as HS and LiDAR data, at the same time generating unbiased and stable classification results in different application fields [64][65][66][67][68]. One of the objectives of this paper was therefore to develop an RFE-RF system, capable of combining the potentiality of RF classification and RFE feature selection, at the same time automatizing the analysis of HS and ALS data for our habitats mapping purpose.…”

Section: Feature Selection and The Recursive Feature Elimination-randmentioning

confidence: 99%

Recursive Feature Elimination and Random Forest Classification of Meadows and Dry Grasslands in Lowland River Valleys of Poland Based on Airborne Hyperspectral and LiDAR Data Fusion

Demarchi¹,

Kania²,

Ciężkowski³

et al. 2020

Preprint

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The use of Hyperspectral (HS) and LiDAR acquisitions has a great potential to enhance mapping and monitoring practices of endangered grasslands habitats, beyond conventional botanical field surveys. In this study we assess the potentiality of Recursive Feature Elimination (RFE) in combination with Random Forest (RF) classification in extracting the main HS and LiDAR features needed to map selected Natura 2000 grasslands along Polish lowland river valleys, in particular alluvial meadows 6440, lowland hay meadows 6510 and xeric and calcareous grasslands 6120. We developed an automated RFE-RF system capable to combine the potentials of both techniques and applied it to multiple acquisitions. Several LiDAR-based products and different Spectral Indexes (SI) were computed and used as input in the system, with the aim of shedding light on the best-to-use features. Results showed a remarkable increase in classification accuracy when LiDAR and SI products are added to the HS dataset, strengthening in particular the importance of employing LiDAR in combination with HS. Using only the 24 optimal features selection generalized over the three study areas, strongly linked to the highly heterogeneous characteristics of the habitats and landscapes investigated, it was possible to achieve rather high classification results (K around 0.7-0.77 and habitats F1 accuracy around 0.8-0.85), indicating that the selected Natura 2000 meadows and dry grasslands habitats can be automatically mapped by airborne HS and LiDAR data. Similar approaches might be considered for future monitoring activities in the context of habitats protection and conservation

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Using recursive feature elimination in random forest to account for correlated variables in high dimensional data

Cited by 256 publications

References 16 publications

A descriptive study of random forest algorithm for predicting COVID-19 patients outcome

A descriptive study of random forest algorithm for predicting COVID-19 patients outcome

Methods for predicting vaccine immunogenicity and reactogenicity

Recursive Feature Elimination and Random Forest Classification of Meadows and Dry Grasslands in Lowland River Valleys of Poland Based on Airborne Hyperspectral and LiDAR Data Fusion

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