Decision Tree Model Based Gene Selection and Classification for Breast Cancer Risk Prediction

Hamim, Mohammed; Moudden, Ismail El; Moutachaouik, Hicham; Hain, Mustapha

doi:10.1007/978-3-030-45183-7_12

Cited by 3 publications

(3 citation statements)

References 20 publications

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“…In comparison with state-of-the-art techniques as shown in Fig. 8, BBHA-RF, PAViCD, and FC5 selection techniques in [11,12,15] select a smaller number of features for Van't veer than the proposed model, but the MI-LASSO-GA achieves higher accuracy. In comparison with selection techniques in [2,8,13] and [23] that use meta-heuristic approaches, the proposed approach obtained higher accuracy with a smaller number of features for Van't veer due to using hybrid approach before meta-heuristic selection technique.…”

Section: Resultsmentioning

confidence: 99%

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A Hybrid Mutual Information-LASSO-Genetic Algorithm Selection Approach for Classifying Breast Cancer

Abdel-Naby

Alfonse

Roushdy

2021

Digital Transformation Technology

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Breast cancer is the most common cancer among women due to many factors such as heredity and unhealthy lifestyles. Early and accurate diagnosis of this cancer improves the patient quality of life and increases the survival rate. Microarray technology provides effective way to early diagnosis cancer. However, the nature of its data complicates the classification process. A hybrid approach of mutual information (MI), least absolute shrinkage and selection operator (LASSO) and genetic algorithm (GA) is proposed to face this challenge. The proposed approach is assessed using logistic regression (LR), support vector machine (SVM), K-nearest neighbor (KNN), and random forest (RF). Compared to the state-of-the-art models, the proposed approach can effectively diagnose the breast cancer with few numbers of genes. For the benchmarked Van't veer dataset, it obtained a classification accuracy of 96% with only 23 features. KeywordsCancer classification • Microarray data • Genetic algorithm • LASSO • Mutual information 1 Introduction Breast cancer is the most commonly occurring cancer in women and the second most common cancer overall. Early detection and diagnosis have been proven to significantly improve patient survival rates [1], quality of life, as well as to significantly reduce the cost and complexity of cancer treatment. Microarray technology has become benchmark technique for early cancer diagnosis [2]. It monitors each gene many times under different conditions or alternately evaluates each gene in M. Abd-elnaby (B) • M.

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Section: Resultsmentioning

confidence: 99%

“…Hamim et al [15] proposed a model to effectively classify breast cancer. Initially, a hybrid selection approach (FC5) of Fisher score (F) and C5.0 was used to select informative genes.…”

Section: Related Workmentioning

confidence: 99%

A Hybrid Mutual Information-LASSO-Genetic Algorithm Selection Approach for Classifying Breast Cancer

Abdel-Naby

Alfonse

Roushdy

2021

Digital Transformation Technology

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show abstract

“…Then in the second phase, they used C5.0 Decision Tree algorithm to find the smallest subset of genes to predict breast cancer with high performance. The experiment results have shown that their prediction framework achieved a performance of 93.28% in term of accuracy by involving only five genes predictors [8].…”

Section: Existing Literaturementioning

confidence: 99%

A Hybrid Gene Selection Strategy Based on Fisher and Ant Colony Optimization Algorithm for Breast Cancer Classification

et al. 2021

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<div id="titleAndAbstract"><p class="0abstract">Breast cancer poses the greatest threat to human life and especially to women's life. Despite the progress made in data mining technology in recent years, the ability to predict and diagnose such fatal diseases based on gene expression data still reveals a limited prediction performance, which may not be surprising since most of the genes in expression data are believed to be irrelevant or redundant. The dimensionality reduction process may be considered as a crucial step to analyze gene expression data, as it can reduce the high dimensionality of the breast cancer datasets, which may result into a better prediction performance of such diseases. The paper suggests a new hybrid approach-based gene selection that combines the filter method and the Ant Colony Optimization algorithm to find the smallest subset of informative genes (genes markers) among 24,481 genes. The proposed approach combines four machine learning algorithms - C5.0 Decision Tree, Support Vector Machines, K-Nearest Neighbors algorithm, and Random Forest Classifier - to classify each of the selected samples (patients) into two classes which have cancer or not. Compared with existing methods in the literature, experimental results indicate that our proposed gene selection approach achieved globally higher classification accuracies with a relatively smaller number of genes.</p></div>

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A Binary Particle Swarm Optimization Based Hybrid Feature Selection Method for Accident Severity Prediction

Hamim,

Enaanai,

Jadli

et al. 2024

Communications in Computer and Information Science

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Decision Tree Model Based Gene Selection and Classification for Breast Cancer Risk Prediction

Cited by 3 publications

References 20 publications

A Hybrid Mutual Information-LASSO-Genetic Algorithm Selection Approach for Classifying Breast Cancer

A Hybrid Mutual Information-LASSO-Genetic Algorithm Selection Approach for Classifying Breast Cancer

A Hybrid Gene Selection Strategy Based on Fisher and Ant Colony Optimization Algorithm for Breast Cancer Classification

A Binary Particle Swarm Optimization Based Hybrid Feature Selection Method for Accident Severity Prediction

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