An Enhanced Breast Cancer Diagnosis Scheme based on Two-Step-SVM Technique

Osman, Ahmed Hamza

doi:10.14569/ijacsa.2017.080423

Cited by 21 publications

(8 citation statements)

References 17 publications

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“…Yue et al examined machine learning (ML) techniques used for the diagnosis and prognostics of breast cancer [20]. The researchers based their study on studies with the aid of SVM [21], NN [22], KNN [23], [24], and DT algorithms [25]. In addition, the breast cancer list from Wisconsin was used.…”

Section: Related Workmentioning

confidence: 99%

“…The alternative nitrate networks is the best so that the accuracy of the prediction by 97.4% [30]. Osman [21] proposed automatic breast cancer diagnostic approach using two-step clustering method and support vector machine algorithm. The hybrid approach aims to improve diagnosis precision and the medical misdiagnosis identification, thus solving breast-tumor-related screening problems.…”

Section: Related Workmentioning

confidence: 99%

“…When analyzed in the UCI-WDBC, the evolved hybrid method improves its precision by 99.1%. [21]. To properly understand hidden breast cancer patterns, the analysis of the sequence of symptoms using machine learning (ML) is an important process.…”

Section: Related Workmentioning

confidence: 99%

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An Effective of Ensemble Boosting Learning Method for Breast Cancer Virtual Screening Using Neural Network Model

Osman

Aljahdali

2020

IEEE Access

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Radial Based Function Neural Network models (RBFNN) are currently used deep-rooted methods for assessing the stages of diagnosis of chronic diseases. The goals of this research are to suggest a model for the diagnosis of breast cancer, and to be able to estimate the stages of development of premalignant breast tumors. The significance of the study is to develop an integrated RBF neural network with ensemble features using the boosting method. The importance of the ensemble boosting method is to generate a sequence of models to achieve more precise predictions. One of the ensemble boosting advantages is that it can take longer to build and to score than a RBF NN standard model. By using ensemble boosting, the accuracy of breast tumor diagnosis increased and thus it became easier to know the stage of the tumor, and whether it was malignant or benign. This will help doctors to select appropriate treatment for each tumor stage, consequently leading to the salvage of cancer patients with this type of tumor. The suggested RBFNN method was examined on the different type of UCI breast cancer datasets. The general diagnosis accuracy based on 10-fold cross validation using RBFNN method obtained 97.4%, 98.4%, 97.7% and 97.0% for the WBC, BCD, BCP, and WBCD UCI datasets respectively. The effectiveness of the proposed method was confirmed by comparing accuracy improvement both before and after using ensemble boosting, and it was found to be more accurate compared with other breast cancer diagnosis methods such as Logistic Regression (91.5%), KNN (96%), SVM (89%), Decision tree (95.13), CNN (97.66%), and Naive Bayes (91%).INDEX TERMS Cancer disease, ensemble boosting, prediction, radial based function, neural network.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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An Effective of Ensemble Boosting Learning Method for Breast Cancer Virtual Screening Using Neural Network Model

Osman

Aljahdali

2020

IEEE Access

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“…Third, when cancer spreads to healthy regions, it's called distant metastasis. Because of their similarities, locoregional recurrence (LLR) is a diagnostic term that may be used to characterize both local and regional recurrence [4]- [6].…”

Section: Introductionmentioning

confidence: 99%

Breast cancer relapse disease prediction improvements with ensemble learning approaches

Sahoo,

Nayak,

Tripathy

et al. 2024

IJEECS

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Diagnosis and prognosis are especially difficult areas of medical research related to cancer due to the high incidence of breast cancer, which has surpassed all other cancers in terms of female mortality. Another factor that has a substantial influence on the quality of life of cancer patients is the fear that they may experience a relapse of their disease. The objective of the study is to give medical practitioners a more effective strategy for using ensemble learning techniques to forecast when breast cancer may recur. This research aimed to investigate the usage of deep neural networks (DNNs) and artificial neural networks (ANNs) in addition to machine learning (ML) based approaches, including bagging, averaging, and voting, to enhance the efficacy of breast cancer relapse diagnosis on two breast cancer relapse datasets. Results from the empirical study demonstrate that the proposed ensemble learning-enabled approach improves accuracies by 96.31% and 95.81%, precisions by 96.70% and 96.15%, sensitivities by 98.88% and 98.68%, specificities by 84.62% in both, F1-scores by 97.78% and 97.40%, and area under the curve (AUCs) of 0.987 and 0.978, with University Medical Centre, Institute of Oncology (UMCIO) and Wisconsin prognostic breast cancer (WPBC) datasets respectively. Consequently, these improved disease outcomes may encourage physicians to use this model to make better treatment choices.

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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.…”

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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An Enhanced Breast Cancer Diagnosis Scheme based on Two-Step-SVM Technique

Cited by 21 publications

References 17 publications

An Effective of Ensemble Boosting Learning Method for Breast Cancer Virtual Screening Using Neural Network Model

An Effective of Ensemble Boosting Learning Method for Breast Cancer Virtual Screening Using Neural Network Model

Breast cancer relapse disease prediction improvements with ensemble learning approaches

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

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