Recursive Feature Elimination with Ridge Regression (L2) Machine Learning Hybrid Feature Selection Algorithm for Diabetic Prediction using Random Forest Classifer.

Venkatachalam, K.; Prabhu, Puniethaa; Abouhawwash, Mohamed; Rajadevi, R.

doi:10.21203/rs.3.rs-742641/v1

Cited by 2 publications

(2 citation statements)

References 13 publications

(13 reference statements)

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“…The author of [31] represented a geometrical approach for analyzing high-dimensional data. An embedded method performs feature selection during the model creation process itself.…”

Section: Embedded Methodsmentioning

confidence: 99%

A Novel Method for Survival Prediction of Hepatocellular Carcinoma Using Feature-Selection Techniques

Ali

Orban

Ramasamy³

et al. 2022

Applied Sciences

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The World Health Organization (WHO) predicted that 10 million people would have died of cancer by 2020. According to recent studies, liver cancer is the most prevalent cancer worldwide. Hepatocellular carcinoma (HCC) is the leading cause of early-stage liver cancer. However, HCC occurs most frequently in patients with chronic liver conditions (such as cirrhosis). Therefore, it is important to predict liver cancer more explicitly by using machine learning. This study examines the survival prediction of a dataset of HCC based on three strategies. Originally, missing values are estimated using mean, mode, and k-Nearest Neighbor (k-NN). We then compare the different select features using the wrapper and embedded methods. The embedded method employs Least Absolute Shrinkage and Selection Operator (LASSO) and ridge regression in conjunction with Logistic Regression (LR). In the wrapper method, gradient boosting and random forests eliminate features recursively. Classification algorithms for predicting results include k-NN, Random Forest (RF), and Logistic Regression. The experimental results indicate that Recursive Feature Elimination with Gradient Boosting (RFE-GB) produces better results, with a 96.66% accuracy rate and a 95.66% F1-score.

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“…The author of [31] represented a geometrical approach for analyzing high-dimensional data. An embedded method performs feature selection during the model creation process itself.…”

Section: Embedded Methodsmentioning

confidence: 99%

A Novel Method for Survival Prediction of Hepatocellular Carcinoma Using Feature-Selection Techniques

Ali

Orban

Ramasamy³

et al. 2022

Applied Sciences

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“…The RF is applied for a classification step. Venkatachalam et al [34] proposed a hybrid method that combined the ridge regression and RFE algorithms. It solved the problem of over-fitting for feature selection.…”

Section: Introductionmentioning

confidence: 99%

Effective hybrid feature selection using different bootstrap enhances cancers classification performance

2022

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Background Machine learning can be used to predict the different onset of human cancers. Highly dimensional data have enormous, complicated problems. One of these is an excessive number of genes plus over-fitting, fitting time, and classification accuracy. Recursive Feature Elimination (RFE) is a wrapper method for selecting the best subset of features that cause the best accuracy. Despite the high performance of RFE, time computation and over-fitting are two disadvantages of this algorithm. Random forest for selection (RFS) proves its effectiveness in selecting the effective features and improving the over-fitting problem. Method This paper proposed a method, namely, positions first bootstrap step (PFBS) random forest selection recursive feature elimination (RFS-RFE) and its abbreviation is PFBS- RFS-RFE to enhance cancer classification performance. It used a bootstrap with many positions included in the outer first bootstrap step (OFBS), inner first bootstrap step (IFBS), and outer/ inner first bootstrap step (O/IFBS). In the first position, OFBS is applied as a resampling method (bootstrap) with replacement before selection step. The RFS is applied with bootstrap = false i.e., the whole datasets are used to build each tree. The importance features are hybrid with RFE to select the most relevant subset of features. In the second position, IFBS is applied as a resampling method (bootstrap) with replacement during applied RFS. The importance features are hybrid with RFE. In the third position, O/IFBS is applied as a hybrid of first and second positions. RFE used logistic regression (LR) as an estimator. The proposed methods are incorporated with four classifiers to solve the feature selection problems and modify the performance of RFE, in which five datasets with different size are used to assess the performance of the PFBS-RFS-RFE. Results The results showed that the O/IFBS-RFS-RFE achieved the best performance compared with previous work and enhanced the accuracy, variance and ROC area for RNA gene and dermatology erythemato-squamous diseases datasets to become 99.994%, 0.0000004, 1.000 and 100.000%, 0.0 and 1.000, respectively. Conclusion High dimensional datasets and RFE algorithm face many troubles in cancers classification performance. PFBS-RFS-RFE is proposed to fix these troubles with different positions. The importance features which extracted from RFS are used with RFE to obtain the effective features.

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Recursive Feature Elimination with Ridge Regression (L2) Machine Learning Hybrid Feature Selection Algorithm for Diabetic Prediction using Random Forest Classifer.

Cited by 2 publications

References 13 publications

A Novel Method for Survival Prediction of Hepatocellular Carcinoma Using Feature-Selection Techniques

A Novel Method for Survival Prediction of Hepatocellular Carcinoma Using Feature-Selection Techniques

Effective hybrid feature selection using different bootstrap enhances cancers classification performance

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