A novel voting convergent difference neural network for diagnosing breast cancer

Zhang, Zhijun; Chen, Bozhao; Xu, Songqing; Chen, Guang-Qiang; Xie, Jilong

doi:10.1016/j.neucom.2021.01.083

Cited by 15 publications

(4 citation statements)

References 39 publications

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“…The result showed that their model reached the highest accuracy rate up to 85% as contrasted to other models. A voting convergent difference neural network (V-CDNN) was proposed by Zhang et al [12] in 2021 to detect breast cancer using the Coimbra dataset. To quantify and assess the relative relevance of various traits, their study used the Gini coefficient, which is based on the random forest algorithm.…”

Section: Related Workmentioning

confidence: 99%

“…Early detection of disease can be achieved by developing a prediction model so that the patient will get better treatment. Machine-learning-based models have been utilized in previous studies for detecting breast cancer and showed significant performance [9][10][11][12][13][14]. Support vector machine (SVM) is an ML model that divides instances of each class from the others by locating the linear optimum hyperplane after nonlinearly mapping the original data into a high-dimensional feature space.…”

Section: Introductionmentioning

confidence: 99%

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Predicting Breast Cancer from Risk Factors Using SVM and Extra-Trees-Based Feature Selection Method

et al. 2022

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Developing a prediction model from risk factors can provide an efficient method to recognize breast cancer. Machine learning (ML) algorithms have been applied to increase the efficiency of diagnosis at the early stage. This paper studies a support vector machine (SVM) combined with an extremely randomized trees classifier (extra-trees) to provide a diagnosis of breast cancer at the early stage based on risk factors. The extra-trees classifier was used to remove irrelevant features, while SVM was utilized to diagnose the breast cancer status. A breast cancer dataset consisting of 116 subjects was utilized by machine learning models to predict breast cancer, while the stratified 10-fold cross-validation was employed for the model evaluation. Our proposed combined SVM and extra-trees model reached the highest accuracy up to 80.23%, which was significantly better than the other ML model. The experimental results demonstrated that by applying extra-trees-based feature selection, the average ML prediction accuracy was improved by up to 7.29% as contrasted to ML without the feature selection method. Our proposed model is expected to increase the efficiency of breast cancer diagnosis based on risk factors. In addition, we presented the proposed prediction model that could be employed for web-based breast cancer prediction. The proposed model is expected to improve diagnostic decision-support systems by predicting breast cancer disease accurately.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Predicting Breast Cancer from Risk Factors Using SVM and Extra-Trees-Based Feature Selection Method

et al. 2022

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“…The Coimbra dataset was used to propose a voting convergent difference neural network (V-CDNN). 16 Various traits were quantified and assessed using the Gini coefficient by using the random forest algorithm. We selected the following input variables based on the experimental data: age, glucose, body mass index, and resistin.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Their model was the most accurate compared to others, achieving 85% accuracy. The Coimbra dataset was used to propose a voting convergent difference neural network (V‐CDNN) 16 . Various traits were quantified and assessed using the Gini coefficient by using the random forest algorithm.…”

Section: Literature Reviewmentioning

confidence: 99%

An intelligent human‐centric systems to diagnose breast cancer using machine learning and optimized feature selection techniques

Kumar,

Singh,

Mahadev

et al. 2023

Trans Emerging Tel Tech

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All over the world, breast cancer (BC) is the leading cause of cancer mortality among women. Computer‐aided methods can assist in early diagnosis. The proposed approach used SMOTE filter with Ch2 test techniques for class balance and feature section using eight different ML models Gaussian Naive Bayes (GNB), Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM) with Linear and Radial Basis Function (RBF), Logistic Regression (LR), K‐nearest neighbor (KNN) and eXtreme Gradient Boosting (XGBoost). A Ch2 test determines the top five features—glucose, HOMA, resistin, BMI, and insulin. Metrics such as accuracy, precision, recall, and F1‐Score are used to compare the performance of models. More than 99% accuracy was achieved by the proposed XGBoost model. Compared to the other breast cancer prediction models, the proposed model had an average accuracy improvement of 9.30%. As a result of our proposed model, breast cancer diagnosis will be more efficient based on risk factors. The proposed prediction model can also predict various breast cancer features. In addition to improving diagnostic decision‐support systems, the proposed model should be able to predict breast cancer disease accurately.

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A Computational Intelligence Approach for Cancer Detection Using Artificial Neural Network

Dash¹,

Dash²,

Rautray³

2022

Smart Innovation, Systems and Technologies

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A novel voting convergent difference neural network for diagnosing breast cancer

Cited by 15 publications

References 39 publications

Predicting Breast Cancer from Risk Factors Using SVM and Extra-Trees-Based Feature Selection Method

Predicting Breast Cancer from Risk Factors Using SVM and Extra-Trees-Based Feature Selection Method

An intelligent human‐centric systems to diagnose breast cancer using machine learning and optimized feature selection techniques

A Computational Intelligence Approach for Cancer Detection Using Artificial Neural Network

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