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Breast cancer is a serious disease that causes death in women if not diagnose early. Early diagnosis of the disease is considered as the best way to save life. Automatic detection of the disease from mammogram image using CAD (computer aided diagnosis) assist radiologist in predicting the disease accurately. In this paper a new model NNC ( Neural Network Classifier) based on artificial neural network is developed for automatically detecting Breast cancer from mammography data. The model NNC is a hybrid of CMAR (Classification based on multiple association rule), Bayes theorem with NN (Neural network) for classifying breast cancer mammographic data as benign or malignant. The model consists of three layers: an input layer, a hidden layer and an output layer. CMAR is used in the initial step for creating the structure of the network and Bayes' Theorem is used for calculating initial weights for hidden layer. It is tested on Wisconsin breast cancer data from UCI repository. Experimental results show that NNC model gives better convergence rate as well as classification accuracy in breast data to both benign and malignant. [ABSTRACT FROM AUTHOR]

• A new ensemble-based classification model for breast cancer detection is proposed. • Benchmark clinical data set is utilized to check the effectiveness of the model. • Most important risk factors of breast cancer were extracted using SVM. • Quite interpretable and accurate clinical decision support is yielded by the model. This paper presents a new data mining technique for an accurate prediction of breast cancer (BC), which is one of the major mortality causes among women around the globe. The main objective of our study is to expand an automatic expert system (ES) to provide an accurate diagnosis of BC. Both, Support Vector Machines (SVMs) and Artificial Neural Networks (ANNs) were applied to analyze BC data. The well-known Wisconsin Breast Cancer Dataset (WBCD), available in the UCI repository, was examined in our study. We first tested the SVM algorithm using various values of the C , ɛ and γ parameters. As a result of the first experiment, we were able to observe that the adjustment of these regularization parameters can greatly improve the performance of the traditional SVM algorithm applied for BC detection. The highest obtained accuracy at the first step was 99.71%. Then, we performed a new BC detection approach based on two ensemble learning techniques: the confidence-weighted voting method and the boosting ensemble technique. Our model, called CWV-BANNSVM, combines boosting ANNs (BANN) and two SVMs, using optimal parameters selected during the first experiment. The performance of the applied methods was evaluated using several popular metrics, such as specificity, sensitivity, precision, FPR, FNR, F 1 score, AUC, Gini and accuracy. The proposed CWV-BANNSVM model was able to improve the performance of the traditional machine learning algorithms applied to BC detection, reaching the accuracy of 100%. To overcome the overfitting issue, we determined and used some appropriate parameter values of polynomial SVM. Our comparison with the existing studies dedicated to BC prediction suggests that the proposed CWV-BANN-SVM model provides one of the best prediction performances overall. [ABSTRACT FROM AUTHOR]

Cancer detection can be formulated as a binary classification in a machine learning paradigm. Loss functions are a critical part of almost every machine learning algorithm. While each loss function comes up with its own advantages and disadvantages, in this paper, inspired by ensemble methods, we propose a novel objective function that is a linear combination of single losses. We then integrate the proposed objective function into an Artificial Neural Network (ANN) to diagnose breast cancer. By doing so, both the coefficients of loss functions and weight parameters of the ANN are learned jointly via backpropagation. As the patients' data are sometimes very noisy, we evaluate our method by doing comprehensive experiments on Wisconsin Breast Cancer Diagnosis (WBCD) dataset at different noise levels. The experiments show its performance declines very slowly (from 0.97 to 0.96) compared to the peer methods with the increase of noise level. [ABSTRACT FROM AUTHOR]

The primary motivation of this paper is twofold: first, to employ a heuristic optimization algorithm to optimize the dendritic neuron model (DNM) and second, to design a tidy visual classifier for computer-aided diagnosis that can be easily implemented on a hardware system. Considering that the backpropagation (BP) algorithm is sensitive to the initial conditions and can easily fall into local minima, we propose an evolutionary dendritic neuron model (EDNM), which is optimized by the gbest-guided artificial bee colony (GABC) algorithm. The experiments are performed on the Liver Disorders Data Set, the Wisconsin Breast Cancer Data Set, the Haberman's Survival Data Set, the Diabetic Retinopathy Debrecen Data Set and Hepatitis Data Set, and the effectiveness of our model was rigorously validated in terms of the classification accuracy, the sensitivity, the specificity, the F_measure, Cohen's Kappa, the area under the receiver operating characteristic curve (AUC), convergence speed and the statistical analysis of the Wilcoxon signed-rank test. Moreover, after training, the EDNM can simplify its neural structure by removing redundant synapses and superfluous dendrites by the neuronal pruning mechanism. Finally, the simplified structural morphology of the EDNM can be replaced by a logic circuit (LC) without sacrificing accuracy. It is worth emphasizing that once implemented by an LC, the model has a significant advantage over other classifiers in terms of speed when handling big data. Consequently, our proposed model can serve as an efficient medical classifier with excellent performance. [ABSTRACT FROM AUTHOR]