Comparative Study of Classification Techniques on Breast Cancer FNA Biopsy Data

You, Haowen; Rumbe, George

doi:10.9781/ijimai.2010.131

Cited by 32 publications

(20 citation statements)

References 12 publications

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“…With the exception of You and Rumbe [15], most related works constructing the SVM classifier for breast cancer prediction are only based on the RBF kernel function. Although RBF is the most widely used kernel function in SVM, the prediction performance obtained using other different popular kernel functions has not yet been fully explored.…”

Section: Literature Reviewmentioning

confidence: 99%

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SVM and SVM Ensembles in Breast Cancer Prediction

et al. 2017

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Breast cancer is an all too common disease in women, making how to effectively predict it an active research problem. A number of statistical and machine learning techniques have been employed to develop various breast cancer prediction models. Among them, support vector machines (SVM) have been shown to outperform many related techniques. To construct the SVM classifier, it is first necessary to decide the kernel function, and different kernel functions can result in different prediction performance. However, there have been very few studies focused on examining the prediction performances of SVM based on different kernel functions. Moreover, it is unknown whether SVM classifier ensembles which have been proposed to improve the performance of single classifiers can outperform single SVM classifiers in terms of breast cancer prediction. Therefore, the aim of this paper is to fully assess the prediction performance of SVM and SVM ensembles over small and large scale breast cancer datasets. The classification accuracy, ROC, F-measure, and computational times of training SVM and SVM ensembles are compared. The experimental results show that linear kernel based SVM ensembles based on the bagging method and RBF kernel based SVM ensembles with the boosting method can be the better choices for a small scale dataset, where feature selection should be performed in the data pre-processing stage. For a large scale dataset, RBF kernel based SVM ensembles based on boosting perform better than the other classifiers.

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Section: Literature Reviewmentioning

confidence: 99%

“…More specifically, studies comparing some of the above mentioned techniques have shown that SVM performs better than many of the other related techniques [10–15]. …”

Section: Introductionmentioning

confidence: 99%

SVM and SVM Ensembles in Breast Cancer Prediction

et al. 2017

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“…Zu et al [8] Optimal Precision Alimi et al [9] Linear, Poly, RBF Precision, Recall, F-Score Xue et al [10] RBF Accuracy Olivares-Mercado et al [12] RBF Precision, Recall, Accuracy, F-Score Joshi et al [59] RBF Accuracy Ahmad et al [16] RBF Accuracy Aruna et al [60] RBF Accuracy Abdelaal et al [15] RBF AUC You and Rumbe [20] Poly, RBF, Sigmoid Accuracy Huang et al [17] RBF Accuracy…”

Section: Studies Kernels Evaluationmentioning

confidence: 99%

“…Among the machine-learning algorithms, such as linear discriminate analysis, decision trees, logistic regression, naïve Bayes, artificial neural networks and k-nearest neighbor, SVM is a tried and tested algorithm that has gained much trust amongst academics [13,14]. Compared to other machine-learning algorithms, SVM stands out to show greater performance [15][16][17][18][19][20], by specifically changing kernel function techniques, such as the polynomial kernel, radial basis function (RBF) kernel and Pearson VII universal function (PUF) kernel. Despite this, only a few researches have shed light on these kernel functions used alongside SVM [13].…”

Section: Introductionmentioning

confidence: 99%

Integrating Qualitative Comparative Analysis and Support Vector Machine Methods to Reduce Passengers’ Resistance to Biometric E-Gates for Sustainable Airport Operations

2019

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For the sake of maintaining sustainable airport operations, biometric e-gates security systems started receiving significant attention from managers of airports around the world. Therefore, how to reduce flight passengers’ perceived resistance to the biometric e-gates security system became much more important than ever. In this sense, the purpose of this study is to analyze the factors which contribute to passenger’s resistance to adopt biometric e-gate technology within the airport security setting. Our focus lies on exploring the effects that perceived risks and benefits as well as user characteristics and propagation mechanisms had on causing such resistance. With survey data from 339 airport users, a support vector machine (SVM) model was implemented to provide a tool for classifying resistance causes correctly, and csQCA (crisp set Qualitative Comparative Analysis) was implemented in order to understand the complex underlying causes. The results showed that the presence of perceived risks and the absence of perceived benefits were the main contributing factors, with propagation mechanisms also showing a significant effect on weak and strong resistance. This study is distinct in that it has attempted to explore innovation adoption through the lens of resistance and in doing so has uncovered important complex causation conditions that need to be considered before service quality can be enhanced within airports. This study’s implications should therefore help steer airport managers in the right direction towards maintaining service quality while implementing sustainable new technologies within their current airport security ecosystem.

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“…Based on the literature review [1,[3][4][5][6][7][8][9][10][11][12], it is obvious that accuracy is the most popular metric for evaluating the performance of the classifier in breast cancer detection. Although the performance of classifier could be different on positive (malignant) and negative (benign) classes, the accuracy cannot make a distinction between false positives and false negatives, and so it does not show the performance of the classifier on positive and negative classes, separately [13].…”

Section: Introductionmentioning

confidence: 99%

The Performance of K-Nearest Neighbors on Malignant and Benign Classes: Sensitivity, Specificity, and Accuracy Analysis for Breast Cancer Diagnosis

Roshanpoor¹,

Ghazisaeidi²,

Kalhor³

et al. 2017

IJCA

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Breast cancer is one of the major threats to women nowadays. Early detection of breast cancer decreases mortality rate. Machine learning algorithms are used for this purpose. Accuracy is the most popular measure for evaluating machine learning algorithms for breast cancer diagnosis. However, it does not make a distinction between the performance of the classifier on malignant and benign test cases. This paper studies sensitivity and specificity along with accuracy to differentiate between KNN performance on malignant and benign classes for the different number of neighbors. Additionally, the standard deviations of sensitivity and specificity are studied to show KNN stability in malignant and benign classes. This study is critical because the cost of false negative is more than the cost of false positive in breast cancer detection. This study is conducted on Wisconsin breast cancer dataset (WBCD) from UCI repository. Stratified 10-fold cross-validation is used in this paper. Additionally, in order to increase the correctness of outcome, validation method repeated 100 times by considering that the samples are randomly reassigned to the folds again. The results show that KNN does not work well on malignant samples compared to the benign test cases, especially for higher values of neighbors. Also, the results for malignant samples are less reliable than benign ones. Furthermore, accuracy is more representative of specificity than sensitivity. It seems that the imbalance distributions of malignant and benign classes make difference between KNN performance on malignant and benign samples. It is recommended that a new study to be conducted to show the effect of imbalance numbers of positive and negative samples and also the difference between standard deviations of positive and negative classes on KNN performance.

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Comparative Study of Classification Techniques on Breast Cancer FNA Biopsy Data

Cited by 32 publications

References 12 publications

SVM and SVM Ensembles in Breast Cancer Prediction

SVM and SVM Ensembles in Breast Cancer Prediction

Integrating Qualitative Comparative Analysis and Support Vector Machine Methods to Reduce Passengers’ Resistance to Biometric E-Gates for Sustainable Airport Operations

The Performance of K-Nearest Neighbors on Malignant and Benign Classes: Sensitivity, Specificity, and Accuracy Analysis for Breast Cancer Diagnosis

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