Breast cancer surgery survivability prediction using bayesian network and support vector machines

Aljawad, Dania Abed; Alqahtani, Ebtesam J.; AL-Kuhaili, Ghaidaa; Qamhan, Nada; Alghamdi, Noof Awad; Alrashed, Saleh; Alhiyafi, Jamal; Olatunji, Sunday O.

doi:10.1109/iciht.2017.7899000

Cited by 15 publications

(14 citation statements)

References 4 publications

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“…Empirical research shows that Support Vector Machine best performs with an accuracy of 74.44% than Bayesian network with an accuracy of 67.56%, Imbalance data is converted into balance. This study helps the doctors to the prediction of the patient stage of cancer using old data as a sample to new data [17]. www.ijacsa.thesai.org P. Hamsagayathri and P. Sampath proposed a Priority Based decision Tree Classifier for Breast cancer.…”

Section: Related Workmentioning

confidence: 99%

Empirical Performance Analysis of Decision Tree and Support Vector Machine based Classifiers on Biological Databases

Amjad¹,

Ali²,

Rafiq³

et al. 2019

IJACSA

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The classification and prediction of medical diseases is a cutting edge research problem in the medical field. The experts of machine learning are continuously proposing new classification methods for the prediction of diseases. The discovery of classification rules from medical databases for classification and prediction of diseases is a challenging and nontrivial task. It is very significant to investigate the more promising and efficient classification approaches for the discovery of classification rules from the medical databases. This paper focuses on the problem of selection of more efficient, promising and suitable classifier for the prediction of specific diseases by performing empirical studies on bunch mark medical databases. The research work under the focus concentrates on the benchmark medical data sets i.e. arrhythmia, breast-cancer, diabetes, hepatitis, mammography, lymph, liver-disorders, sick, cardiotocography, heart-statlog, breast-w, and lung-cancer. The medical data sets are obtained from the open-source UCI machine learning repository. The research work will be investigating the performance of Decision Tree (i.e. AdaBoost.NC, C45-C, CART, and ID3-C) and Support Vector Machines. For experimentation, Knowledge Extraction based on Evolutionary Learning (KEEL), a data mining tool will be used. This research work provides the empirical performance analysis of decision tree-based classifiers and SVM on a specific dataset. Moreover, this article provides a comparative performance analysis of classification approaches in terms of statistics.

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Section: Related Workmentioning

confidence: 99%

Empirical Performance Analysis of Decision Tree and Support Vector Machine based Classifiers on Biological Databases

Amjad¹,

Ali²,

Rafiq³

et al. 2019

IJACSA

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“…The leading cause of death in women worldwide was Breast cancer [1,2], the second most common cancer across the world after lung cancer. The odds of recovery are better when diagnosed in the early stages [3].…”

Section: Introductionmentioning

confidence: 99%

“…The advantages of K-Means make it famous. The most significant ones are flexibility and ease of use [2]. Besides, it has linear complexity in space and is generally fast.…”

Section: Introductionmentioning

confidence: 99%

Microarray Breast Cancer Data Clustering Using Map Reduce Based K-Means Algorithm

Thottathyl¹,

Kanadam²,

Panchadula³

2020

RIA

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Breast cancer is one of the world's most advanced and most common cancers occurring in women. An early diagnosis of breast cancer offers treatment for it; therefore, several experiments are in development establishing approaches for the early detection of breast cancer. The great increase in research in the last decade in microarray data processing is a potent tool of diagnosing diseases. Based on genomic knowledge, micro-arrays have changed the way clinical pathology recognizes, identifies, and classifies the diseases of humans, particularly those of cancer. In this article, we examined microarray data for breast cancer with the k-means clustering algorithm, but it was hard to scale and process a large number of micro-array data alone. To this end, we use a chart to minimize the paradigm for evaluating microarray data on breast cancer. Moreover, the efficiency of the parallel k-means model is measured with the operating period, the scaling, and all runtime of the model.

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“…Actually, by employing data mining techniques and data modeling, cancer patients with dangerous INTRODUCTION conditions might be diagnosed [2]. For example, in recent studies, the precision and accuracy of sequential minimal optimization (SMO) and SVM algorithms in diagnosing breast cancer were evaluated [4][5][6][7][8]. The studies indicated that the SVM and SMO algorithms were superior to other algorithms in increasing the precision level of diagnosis.…”

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confidence: 99%

“…The studies indicated that the SVM and SMO algorithms were superior to other algorithms in increasing the precision level of diagnosis. In a study carried out by Aljawad et al, two BN and SVM algorithms were evaluated with regards to the prediction of conservation status in patients with breast cancer [4]. The investigated criteria were precision, accuracy, and recall.…”

mentioning

confidence: 99%

The Use of the Binary Bat Algorithm in Improving the Accuracy of Breast Cancer Diagnosis

Yaghoubzadeh

Tabbakh

Barzgar

et al. 2021

Multidiscip Cancer Investig

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Breast cancer surgery survivability prediction using bayesian network and support vector machines

Cited by 15 publications

References 4 publications

Empirical Performance Analysis of Decision Tree and Support Vector Machine based Classifiers on Biological Databases

Empirical Performance Analysis of Decision Tree and Support Vector Machine based Classifiers on Biological Databases

Microarray Breast Cancer Data Clustering Using Map Reduce Based K-Means Algorithm

The Use of the Binary Bat Algorithm in Improving the Accuracy of Breast Cancer Diagnosis

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