Association, statistical, mathematical and neural approaches for mining breast cancer patterns

Pendharkar, Parag C.; Rodger, James A.; Yaverbaum, Gayle J.; Suryana, Nanna; Benner, Marilen

doi:10.1016/s0957-4174(99)00036-6

Cited by 98 publications

(36 citation statements)

References 25 publications

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“…Over the past few decades, ANNs have been employed increasingly by more and more researchers, and become an active research area [37][38][39][40]. ANNs have afforded numerous successes with great progress in BC classification and diagnosis in the very early stages [22,[41][42][43][44][45][46][47]. A typical ANN model is made up of a hierarchy of layers: input, hidden and output layers ( Figure 1) [48].…”

Section: Approachesmentioning

confidence: 99%

Machine Learning with Applications in Breast Cancer Diagnosis and Prognosis

Yue

Wang

Chen

et al. 2018

Designs

212

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Breast cancer (BC) is one of the most common cancers among women worldwide, representing the majority of new cancer cases and cancer-related deaths according to global statistics, making it a significant public health problem in today's society. The early diagnosis of BC can improve the prognosis and chance of survival significantly, as it can promote timely clinical treatment to patients. Further accurate classification of benign tumours can prevent patients undergoing unnecessary treatments. Thus, the correct diagnosis of BC and classification of patients into malignant or benign groups is the subject of much research. Because of its unique advantages in critical features detection from complex BC datasets, machine learning (ML) is widely recognised as the methodology of choice in BC pattern classification and forecast modelling. In this paper, we aim to review ML techniques and their applications in BC diagnosis and prognosis. Firstly, we provide an overview of ML techniques including artificial neural networks (ANNs), support vector machines (SVMs), decision trees (DTs), and k-nearest neighbors (k-NNs). Then, we investigate their applications in BC. Our primary data is drawn from the Wisconsin breast cancer database (WBCD) which is the benchmark database for comparing the results through different algorithms. Finally, a healthcare system model of our recent work is also shown.

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Section: Approachesmentioning

confidence: 99%

Machine Learning with Applications in Breast Cancer Diagnosis and Prognosis

Yue

Wang

Chen

et al. 2018

Designs

212

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“…Pendharkar et al used several data mining techniques for exploring patterns in breast cancer 9 . In the past usage of the Ljubljana dataset, an accuracy range of 66% to 78% has been achieved by several researchers.…”

Section: Literature Surveymentioning

confidence: 99%

Accuracy Evaluation of C4.5 and Naive Bayes Classifiers Using Attribute Ranking Method

Sivakumari¹,

Priyadarsini²,

Amudha³

2009

IJCIS

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This paper intends to classify the Ljubljana Breast Cancer dataset using C4.5 Decision Tree and Naïve Bayes classifiers. In this work, classification is carriedout using two methods. In the first method, dataset is analysed using all the attributes in the dataset. In the second method, attributes are ranked using information gain ranking technique and only the high ranked attributes are used to build the classification model. We are evaluating the results of C4.5 Decision Tree and Naïve Bayes classifiers in terms of classifier accuracy for various folds of cross validation. Our results show that both the classifiers achieve good accuracy on the dataset.

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“…Current computer-based medical diagnostic methods use neural networks, discriminant analysis and other machine learning approaches for medical diagnosis (Pendharkar et al 1999). Most of these approaches do not allow the decision maker to incorporate the information acquisition cost constraints.…”

Section: Extended Abstractmentioning

confidence: 99%

Hybrid approaches for classification under information acquisition cost constraint

Pendharkar

2005

Decision Support Systems

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Association, statistical, mathematical and neural approaches for mining breast cancer patterns

Cited by 98 publications

References 25 publications

Machine Learning with Applications in Breast Cancer Diagnosis and Prognosis

Machine Learning with Applications in Breast Cancer Diagnosis and Prognosis

Accuracy Evaluation of C4.5 and Naive Bayes Classifiers Using Attribute Ranking Method

Hybrid approaches for classification under information acquisition cost constraint

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