Using data mining for assessing diagnosis of breast cancer

Abdelaal, Medhat Mohamed Ahmed; Farouq, Muhamed Wael; Sena, Hala Abou; Salem, Abdel-Badeeh M.

doi:10.1109/imcsit.2010.5679647

Cited by 23 publications

(10 citation statements)

References 5 publications

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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%

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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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Section: Studies Kernels Evaluationmentioning

confidence: 99%

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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“…IV. CLASSIFICATION Artificial neural network (ANN) classifier is used for classification in the proposed system as it is state of the art tool for pattern classification and widely used in similar applications [21][22][23][24][25][26]. A Neural network is composed of simple parallel elements that are inspired by nodes of the biological nervous system.…”

Section: Feature Subset Selectionmentioning

confidence: 99%

Breast Cancer Classification using Global Discriminate Features in Mammographic Images

Nadeem¹,

Abid²,

Qadeer³

et al. 2019

ijacsa

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Breast cancer has become a rapidly prevailing disease among women all over the world. In term of mortality, it is considered to be the second leading cause of death. Death risk can be reduced by early stage detection, followed by a suitable treatment procedure. Contemporary literature shows that mammographic imaging is widely used for premature discovery of breast cancer. In this paper, we propose an efficient Computer Aided Diagnostic (CAD) system for the detection of breast cancer using mammography images. The CAD system extracts largely discriminating features on the global level for representation of target categories in two sets: all 20 extracted features and top 7 ranked features among them. Texture characteristics using cooccurrence matrices are calculated via the single offset vector. Multilayer perceptron neural network with optimized architecture is fed with individual feature sets and results are produced. Data division corresponds as 60%, 20%, and 20% is used for training, cross-validation, and test purposes, respectively. Robust results are achieved and presented after rotating the data up to five times, which shows higher than 99% accuracy for both target categories, and hence outperform the existing solutions.

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“…Gupta et al (2011) analyzes the various classification techniques applied to diagnosis and prognosis of breast cancer. The author analyse the papers (Sarvestan et al, 2010;Orlando et al, 2010;Abdelaal et al, 2010;Chang and Liou, 2005;Gandhi et al, 2010;Padmavati, 2011;Chul et al, 2001;Hassanien and Jafar, 2004;Sudhir et al, 2006;Jamarani et al, 2005;Abdelghani and Guven, 2006;Choi et al, 2009;Lundin et al, 1999;Street, 1998;Chi et al, 2007;Dursun et al, 2004;Khan et al, 2008) and concludes that any classification method is acceptable for diagnosis. But for the prognosis ANN classification method gives higher accuracy than any other classification methods.…”

Section: Cancer Datasetmentioning

confidence: 99%

“…8. The maximum accuracy of an algorithm gained in each research work for diagnoses and prognoses of breast cancer from (Pitchumani and Kamal, 2011;Paulin and Santhakumaran, 2011;Rajesh and Anand, 2012;Thangavel and Mohideen, 2009;Gupta et al, 2011;Sarvestan et al, 2010;Orlando et al, 2010;Abdelaal et al, 2010;Chang and Liou, 2005;Gandhi et al, 2010;Padmavati, 2011;Chul et al, 2001;Hassanien and Jafar, 2004;Sudhir et al, 2006;Jamarani et al, 2005;Choi et al, 2009;Lundin et al, 1999;Street, 1998;Chi et al, 2007;Dursun et al, 2004;Khan et al, 2008;Burke et al, 1997;Chang and Liouc, 2002;Othman and Yau, 2007;Shweta, 2012) are taken as the input data values and is used for the comparative analysis which is shown in Fig. 8.…”

Section: Experimental and Comparative Studymentioning

confidence: 99%

Performance Analysis of Classification Algorithms on Medical Diagnoses-a Survey

Vanaja¹,

Rameshkumar²

2015

Journal of Computer Science

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The aim of this research paper is to study and discuss the various classification algorithms applied on different kinds of medical datasets and compares its performance. The classification algorithms with maximum accuracies on various kinds of medical datasets are taken for performance analysis. The result of the performance analysis shows the most frequently used algorithms on particular medical dataset and best classification algorithm to analyse the specific disease. This study gives the details of different classification algorithms and feature selection methodologies. The study also discusses about the data constraints such as volume and dimensionality problems. This research paper also discusses the new features of C5.0 classification algorithm over C4.5 and performance of classification algorithm on high dimensional datasets. This research paper summarizes various reviews and technical articles which focus on the current research on Medical diagnosis.

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Using data mining for assessing diagnosis of breast cancer

Cited by 23 publications

References 5 publications

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

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

Breast Cancer Classification using Global Discriminate Features in Mammographic Images

Performance Analysis of Classification Algorithms on Medical Diagnoses-a Survey

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