Review On Feature Selection Techniques And The Impact Of Svm For Cancer Classification Using Gene Expression Profile

George, G. Victo Sudha; Raj, V. Cyril

doi:10.5121/ijcses.2011.2302

Cited by 46 publications

(12 citation statements)

References 38 publications

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“…Feature selection techniques measure the importance of a feature or a set of features according to a given measure. There are many goals of these techniques, but the most important ones are [36] Consider now a feature ranking algorithm that leads to a ranking vector r with components r = (r1, r2, r3, . .…”

Section: Methodsmentioning

confidence: 99%

“…Feature selection is a key step in many classification problems [17,36,4].The size of the training data set needed to calibrate a model grows exponentially with the number of dimensions but the number of instances may be limited due to the cost of data collection. In particular, in cancer risk prediction applications, reducing the data dimensionality can avoid overfitting and improve model performance [3,10,27,11].…”

Section: Introductionmentioning

confidence: 99%

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Assessing Feature Selection Techniques for a Colorectal Cancer Prediction Model

Cueto-López

Aláiz-Rodríguez

García-Ordás

et al. 2017

Advances in Intelligent Systems and Computing

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Background and objective: Risk prediction models aim at identifying people at higher risk of developing a target disease. Feature selection is particularly important to improve the prediction model performance avoiding overfitting and to identify the leading cancer risk (and protective) factors. Assessing the stability of feature selection/ranking algorithms becomes an important issue when the aim is to analyze the features with more prediction power. Methods: This work is focused on colorectal cancer, assessing several feature ranking algorithms in terms of performance for a set of risk prediction models (Neural Networks, Support Vector Machines (SVM), Logistic Regression, k-Nearest Neighbors and Boosted Trees). Additionally, their robustness is evaluated following a conventional approach with scalar stability metrics and a visual approach proposed in this work to study both similarity among fea- *

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Assessing Feature Selection Techniques for a Colorectal Cancer Prediction Model

Cueto-López

Aláiz-Rodríguez

García-Ordás

et al. 2017

Advances in Intelligent Systems and Computing

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“…Feature selection methods evaluate the importance of a feature or group of features based on a predetermined metric. The most important benefits of these strategies are: (a) to prevent overfitting and improve model performance, (b) to acquire a more precise and more comprehensive understanding of the problem, and (c) to create faster and more cost-effective prediction models [30]. We included all available features in the dataset for the initial run to establish a performance baseline and measure the processing time.…”

Section: Feature Selectionmentioning

confidence: 99%

Improving Cardiovascular Disease Prediction by Integrating Imputation, Imbalance Resampling, and Feature Selection Techniques into Machine Learning Model

Alfebi

Anasanti

2023

Indonesian J. Comput. Cybern. Syst.

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Cardiovascular disease (CVD) is the leading cause of death worldwide. Primary prevention is by early prediction of the disease onset. Using laboratory data from the National Health and Nutrition Examination Survey (NHANES) in 2017-2020 timeframe (N= 7.974), we tested the ability of machine learning (ML) algorithms to classify individuals at risk. The ML models were evaluated based on their classification performances after comparing four imputation, three imbalance resampling, and three feature selection techniques.Due to its popularity, we utilized decision tree (DT) as the baseline. Integration of multiple imputation by chained equation (MICE) and synthetic minority oversampling with Tomek link down-sampling (SMOTETomek) into the model improved the area under the curve-receiver operating characteristics (AUC-ROC) from 57% to 83%. Applying simultaneous perturbation feature selection and ranking (spFSR) reduced the feature predictors from 144 to 30 features and the computational time by 22%. The best techniques were applied to six ML models, resulting in Xtreme gradient boosting (XGBoost) achieving the highest accuracy of 93% and AUC-ROC of 89%.The accuracy of our ML model in predicting CVD outperforms those from previous studies. We also highlight the important causes of CVD, which might be investigated further for potential effects on electronic health records.

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“…In the context of the microarray technology, feature selection can be organized into three categories [14,15,18,19,27]: filter, wrapper, and embedded. In the filter method; the genes are evaluated and ranked against the class label and it does not take into considering the correlation and the interaction between the genes.…”

Section: Background and Literature Reviewmentioning

confidence: 99%

“…A major technological advance in classifying cancers has been the development of DNA microarray techniques, which have enabled the simultaneous measurement of a large number of genes' expression levels [10,[14][15][16]. The big challenge that faces the high dimensionality of genes (features) compared to the limited sample size available [6,9,14,[17][18][19][20][21][22][23][24].…”

Section: Introductionmentioning

confidence: 99%

A framework model using multifilter feature selection to enhance colon cancer classification

Al-Rajab

2021

PLoS ONE

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Gene expression profiles can be utilized in the diagnosis of critical diseases such as cancer. The selection of biomarker genes from these profiles is significant and crucial for cancer detection. This paper presents a framework proposing a two-stage multifilter hybrid model of feature selection for colon cancer classification. Colon cancer is being extremely common nowadays among other types of cancer. There is a need to find fast and an accurate method to detect the tissues, and enhance the diagnostic process and the drug discovery. This paper reports on a study whose objective has been to improve the diagnosis of cancer of the colon through a two-stage, multifilter model of feature selection. The model described deals with feature selection using a combination of Information Gain and a Genetic Algorithm. The next stage is to filter and rank the genes identified through this method using the minimum Redundancy Maximum Relevance (mRMR) technique. The final phase is to further analyze the data using correlated machine learning algorithms. This two-stage approach, which involves the selection of genes before classification techniques are used, improves success rates for the identification of cancer cells. It is found that Decision Tree, K-Nearest Neighbor, and Naïve Bayes classifiers had showed promising accurate results using the developed hybrid framework model. It is concluded that the performance of our proposed method has achieved a higher accuracy in comparison with the existing methods reported in the literatures. This study can be used as a clue to enhance treatment and drug discovery for the colon cancer cure.

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Review On Feature Selection Techniques And The Impact Of Svm For Cancer Classification Using Gene Expression Profile

Abstract: ABSTRACT

Cited by 46 publications

References 38 publications

Assessing Feature Selection Techniques for a Colorectal Cancer Prediction Model

Assessing Feature Selection Techniques for a Colorectal Cancer Prediction Model

Improving Cardiovascular Disease Prediction by Integrating Imputation, Imbalance Resampling, and Feature Selection Techniques into Machine Learning Model

A framework model using multifilter feature selection to enhance colon cancer classification

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