Wisconsin Breast Cancer Diagnosis Research Articles

Breast Cancer Staging Prediction Based on Logistic Regression Model

Breast cancer is one of the most common diseases in women. It is important to diagnose whether a patient’s breast cancer is benign or malignant as early as possible because there are different treatments for different stages. If the spread of cancer and tumors can be controlled early, patient’s suffering can be reduced and survival rates improved. In order to determine if a patient has benign or malignant breast cancer, this article will develop a breast cancer staging prediction model using a popular machine learning approach called logistic regression. The Wisconsin Breast Cancer Diagnosis (WBCD) dataset, provided by the University of Irvine Machine Learning, is the basis for the logistic regression model used in this study. A confusion matrix is utilized to assess the model’s accuracy as well as Type I and Type II errors. The Type I and Type II errors’ percentages are very small, the accuracy of the logistic regression model is 94.958%, which is not very high thus it may not be recommended for people to use and rely on this model to predict breast cancer, people can consider other prediction models. Furthermore, there may be several factors which may lead to the low logistic regression model’s accuracy, such as the size of the database, selection of samples, or the selection of variables.

Impressive predictive model for Breast Cancer based on Machine Learning

INTRODUCTION: Breast cancer is a major health concern for women all over the world.
 OBJECTIVES: In order to reduce mortality rates and provide the most effective treatment, Histopathology image prognosis is essential. When a pathologist examines a biopsy specimen under a microscope, they are engaging in histopathology. The pathologist looks for the picture, determines its type, labels it, and assigns a grade.
 METHODS: Tissue architecture, cell distribution, and cellular form all play a role in determining whether a histopathological scan is benign or malignant. Manual picture classification is the slowest and most error-prone method. Automated diagnosis based on machine learning is necessary for early and precise diagnosis, but this challenge has prevented it from being addressed thus far. In this study, we apply curvelet transform to a picture that has been segmented using k-means clustering to isolate individual cell nuclei.
 RESULTS: We analysed data from the Wisconsin Diagnosis Breast Cancer database for this article in the context of similar studies in the literature.
 CONCLUSION: It is demonstrated that compared to another machine learning algorithm, the IICA-ANN IICA-KNN and IICA-SVM-KNN method using the logistic algorithm achieves 98.04% accuracy.

Effective Feature Engineering and Classification of Breast Cancer Diagnosis: A Comparative Study

Breast cancer is among the most common cancers found in women, causing cancer-related deaths and making it a severe public health issue. Early prediction of breast cancer can increase the chances of survival and promote early medical treatment. Moreover, the accurate classification of benign cases can prevent cancer patients from undergoing unnecessary treatments. Therefore, the accurate and early diagnosis of breast cancer and the classification into benign or malignant classes are much-needed research topics. This paper presents an effective feature engineering method to extract and modify features from data and the effects on different classifiers using the Wisconsin Breast Cancer Diagnosis Dataset. We then use the feature to compare six popular machine-learning models for classification. The models compared were Logistic Regression, Random Forest, Decision Tree, K-Neighbors, Multi-Layer Perception (MLP), and XGBoost. The results showed that the Decision Tree model, when applied to the proposed feature engineering, was the best performing, achieving an average accuracy of 98.64%.

Breast Cancer Diagnosis Using a Novel Parallel Support Vector Machine with Harris Hawks Optimization

Three contributions are proposed. Firstly, a novel hybrid classifier (HHO-SVM) is introduced, which is a combination between the Harris hawks optimization (HHO) and a support vector machine (SVM) is introduced. Second, the performance of the HHO-SVM is enhanced using the conventional normalization method. The final contribution is to improve the efficiency of the HHO-SVM by adopting a parallel approach that employs the data distribution. The proposed models are evaluated using the Wisconsin Diagnosis Breast Cancer (WDBC) dataset. The results show that the HHO-SVM achieves a 98.24% accuracy rate with the normalization scaling technique, outperforming other related works. On the other hand, the HHO-SVM achieves a 99.47% accuracy rate with the equilibration scaling technique, which is better than other previous works. Finally, to compare the three effective scaling strategies on four CPU cores, the parallel version of the proposed model provides an acceleration of 3.97.

A feature selection using improved dragonfly algorithm with support vector machine for breast cancer prediction

ABSTRACT Breast cancer is the most common cancer-related death in women, accounting for 16% of all cancer-related fatalities globally. Breast cancer is fatal in just half of all cases. Radiologists may misread worrisome lesions due to imaging quality concerns and diverse breast densities, which raises the false-(positive and negative) ratio, as the primary explanation for the problem. Early intervention is critical in building a current prognosis process that can successfully limit disease consequences and increase recovery. Patients are being referred back for biopsies to dispel suspicions when the inconsistent feature-extraction approach is used for manual screening of breast abnormalities in traditional schemes. With the Multi-kernel Support Vector Machine (MKSVM), we develop a new modality for the prediction of breast cancer and train its properties using the supervised machine learning approaches. Furthermore, the IDA-MKSVM technique achieved maximum average accuracy is 95.75%, average sensitivity is 94.29%, average specificity is 95.16% and average F-score is 95.39% for different training datasets. For selecting ideal features, the Improved Dragonfly Algorithm (IDA) is also used. A 10-fold cross validation procedure is used in the system under consideration to ensure accuracy. The UCI machine learning repository holds the breast cancer diagnosis data referred to as the Wisconsin breast cancer diagnosis data set.

Effective kernel‐principal component analysis based approach for wisconsin breast cancer diagnosis

This work aims to identify cancerous (malignant) and non-malignant (non-cancerous) cells in a breast cancer database. Wisconsin breast cancer data (WBC) was utilized and obtained from the University of California, Irvine's machine learning repository. The proposed approach involves the Naive bayes algorithm with Gaussian distribution of the function in combination with Chi-squared-based attributes selection approach. This experimentation has been done after reducing the dimensional space of the used data with extended Kernel Principal Component Analysis (K-PCA). Five different kernels in K-PCA have been tested after the implementation of necessary pre-processing techniques. The performance assessment of the proposed system has been evaluated based on confusion matrix-based accuracy, precision, sensitivity, and specificity. Our proposed methodology with six selected feature and sigmoid K-PCA attained the best accuracy of 99.28%. This result outer performs many state-of-the-art studies recently published on the identical dataset.

BIO-INSPIRED ENSEMBLE FEATURE SELECTION (BIEFS) AND ENSEMBLE MULTIPLE DEEP LEARNING (EMDL) CLASSIFIER FOR BREAST CANCER DIAGNOSIS

Breast cancer is a major disease diagnosed in women, affecting 2.1 million women every year, and is the reason for most cancer-related mortality in women, as per the World Health Organization (WHO). For cancer researchers, accurately forecasting the life expectancy of breast cancer patients is a serious challenge. Bio-Inspired Ensemble Feature Selection (BIEFS) algorithm is introduced uses three feature selection methods such as Adaptive Mutation Enhanced Elephant Herding Optimization (AMEHO), Adaptive Mutation Butterfly Optimization Algorithm (AMBOA), and Adaptive Salp Swarm Algorithm (ASSA) and integrates their normalized outputs for getting quantitative ensemble importance. BIEFS algorithm depends upon the aggregation of multiple FS techniques by Pearson Correlation Coefficient (PCC). Ensemble Multiple Deep Learning (EMDL) classifier is introduced which combines several individual models such as AdaBoost-Convolutional Neural Network (A-CNN), Long Short-Term Memory Network (LSTM), and Deep Auto-Encoder (DAE) is introduced to obtain better generalization performance. Then, these three classifiers such as A-CNN, LSTM, and DAE are used for ensemble classification using a Weight Majority Voting (WMV) mechanism. Wisconsin Diagnosis Breast Cancer (WOBC), and Wisconsin Diagnosis Breast Cancer (WDBC) are collected from University of California, Irvine (UCI) repository for experimentation. Evaluation metrics like Precision, Recall, F-measure, Accuracy, and Area Under Curve (AUC) are used to compare the results of proposed system and existing classifiers. All experiments are executed within a simulation environment and conducted in MATrix LABoratory R 2014 a (MATLAB 2014a) tool. The proposed EMDL classifier is showing better performance as compared to the traditional classification models.

Application of Particle Swarm Optimization on Wisconsin Diagnosis Breast Cancer Dataset

Application of Particle Swarm Optimization on Wisconsin Diagnosis Breast Cancer Dataset

Breast Cancer Detection Using Machine Algorithms

The most frequently occurring cancer among Indian women is breast cancer. There is a chance of fifty percent for fatality in a case as one of two women diagnosed with breast cancer deaths in the cases of Indian women. This paper aims to present a comparison of the largely popular machine learning algorithms and techniques commonly used for breast cancer prediction, namely Random Forest, KNN (k-Nearest-Neighbor), Support Vector Machine, and XG Boost techniques. The Wisconsin Diagnosis Breast Cancer data set was used as a training set to compare the performance of the various machine learning techniques in terms of key parameters such as accuracy and precision. The results obtained are very competitive and can be used for detection and treatment. Breast cancer disease causes a massive number of deaths in the world. After the traditional cancer detection methods, the latest technologies enable experts with numerous adaptive methods to discover breast cancer in women. Breast cancer affects the majority of women worldwide, and it is the second most common cause of death among women. Breast cancer is among the most serious illnesses/diseases in India, causing many deaths in the current situation. Due to changes in food and lifestyle, the number of cancer cases in women is increasing day by day. Different types of machine learning are implemented for the prediction of breast cancer with a high accuracy rate. We develop different machine learning algorithms for the prediction of breast cancer.

Breast Cancer Diagnosis Using Support Vector Machines Optimized by Whale Optimization and Dragonfly Algorithms

Breast Cancer (BC) has become a critical illness with a high mortality rate during the previous decade. It is considered the women’s most common cancer. In this paper, we propose two optimum automated BC classification approaches based on a hybridization of the Whale Optimization Algorithm (WOA) and Dragonfly Algorithm (DA), with Radial Basis Function Kernel Support Vector Machines (RBF-SVM), to increase the accuracy of BC classification (CA) by determining the optimum SVM parameters. The effectiveness of the proposed WOA-SVM and DA-SVM algorithms is tested on the Wisconsin Diagnosis Breast Cancer (WDBC) databases and the Wisconsin Breast Cancer Database (WBCD). Various metric parameters such as CA, confusion matrix, the area under the ROC curve (AUC), sensitivity, and specificity are utilized to assess and consider the effectiveness of the proposed approaches. The results are compared not only to the most common optimizers, Particle Swarm Optimization (PSO) and Genetic Algorithm (GA), that are used for training SVM and artificial neural networks (ANN) classifiers, but also to other classification models. The WOA-SVM and DA-SVM are also explored for feature selection, and their findings are compared to the offered models. According to the experimental results, the proposed WOA-SVM method outperforms previous classification approaches on the WBCD dataset. On the WDBC dataset, however, the proposed DA-SVM algorithm outperforms the previous classification algorithms. Using typical datasets’ partition, the resultant CA is as high as 99.65% and 100% for WDBC and WBCD, respectively. However, using a 10-fold cross-validation datasets’ partition, the mean resultant CA are 97.89% and 99.27%, respectively.

A Breast Cancer Diagnosis Method Based on VIM Feature Selection and Hierarchical Clustering Random Forest Algorithm

Breast cancer is a neoplastic disease which seriously threatens women’s health. It is regard as the most common cause of cancer death in women. Accurate detection and effective treatment are of vital significance to lower the death rate of breast cancer. In recent years, machine learning technique has been considered to be an effective method for accurate diagnosis of various diseases, among which Random Forest (RF) has been widely applied. However, decision trees with poor classification performance and high similarity may be generated during the training process, which affects the overall classification performance of the model. In this paper, a Hierarchical Clustering Random Forest (HCRF) model is developed. By measuring the similarity among all the decision trees, the hierarchical clustering technique is used to carry out clustering analysis on decision trees. The representative trees are selected from divided clusters to construct the hierarchical clustering random forest with low similarity and high accuracy. In addition, we use Variable Importance Measure (VIM) method to optimize the selected feature number for the breast cancer prediction. Wisconsin Diagnosis Breast Cancer (WDBC) database and Wisconsin Breast Cancer (WBC) database from the UCI (University of California Irvine) Machine Learning repository are employed in this study. The performance of the proposed method is evaluated by utilizing accuracy, precision, sensitivity, specificity and AUC (Area Under ROC Curve). Experimental results indicate that the classification based on HCRF algorithm with VIM as a feature selection method reaches the best accuracy of 97.05% and 97.76% compared to Decision Tree, Adaboost and Random Forest on both the WDBC and WBC datasets. The method proposed in this study is an effective tool for diagnosing breast cancer.

Support Vector Machine Classifier For Prediction Of Breast Malignancy Using Wisconsin Breast Cancer Dataset

Cancer is the world's second largest cause of death. In 2018, 9.6 million people died from cancer. In any medical sickness, breast cancer is one of the most delicate and endemic diseases. This is one of the primary causes of female death in the world. Breast cancer kills one out of every eleven women around the world. "Early detection equals improved odds of survival," says a well-known cancer adage. As a result, early detection is essential for successfully preventing breast cancer and lowering morality. Breast Cancer is a type of cancer that affects one of the most significant issues that humanity has faced in recent decades has been diagnosis and prediction. Cancer detection that is accurate can save millions of lives. Effective technologies for diagnosing malignant breasts aid healthcare providers in diagnosing and treating patients in a fast and accurate manner. Experiments were carried out in this study to categorise breast cancer as benign or malignant using the Wisconsin Diagnosis Breast Cancer (WDBC) database. Support Vector Machine is a supervised learning technique (SVM). The SVM classifier's classification performance is evaluated. Experiments demonstrate that the SVM model has a fantastic performance, with a classification accuracy of 96.09 percent on the testing subset.

Support Vector Machine Classifier For Prediction Of Breast Malignancy Using Wisconsin Breast Cancer Dataset

Cancer is the world's second largest cause of death. In 2018, 9.6 million people died from cancer. In any medical sickness, breast cancer is one of the most delicate and endemic diseases. This is one of the primary causes of female death in the world. Breast cancer kills one out of every eleven women around the world. "Early detection equals improved odds of survival," says a well-known cancer adage. As a result, early detection is essential for successfully preventing breast cancer and lowering morality. Breast Cancer is a type of cancer that affects one of the most significant issues that humanity has faced in recent decades has been diagnosis and prediction. Cancer detection that is accurate can save millions of lives. Effective technologies for diagnosing malignant breasts aid healthcare providers in diagnosing and treating patients in a fast and accurate manner. Experiments were carried out in this study to categorise breast cancer as benign or malignant using the Wisconsin Diagnosis Breast Cancer (WDBC) database. Support Vector Machine is a supervised learning technique (SVM). The SVM classifier's classification performance is evaluated. Experiments demonstrate that the SVM model has a fantastic performance, with a classification accuracy of 96.09 percent on the testing subset.

Clinical diagnosis of breast cancer samples using artificial immune systems with negative selection / Diagnóstico clínico de amostras de cancro da mama utilizando sistemas imunitários artificiais com selecção negativa

This paper uses an artificial immune system applied for diagnosing breast cancer samples. Taking as basis an immunological process, the negative selection algorithm was used to discriminate the samples, attaining a classification for benign or malignant cases. The main application of the method is to assist professionals in the breast cancer diagnostic process, thereby providing decision-making agility, efficient treatment planning, reliability and the necessary intervention to save lives. To evaluate this method, the wisconsin breast cancer diagnosis database was used. This is an actual breast cancer database. The results obtained using the method, when compared with the specialized literature, show accuracy, robustness and efficiency in the breast cancer diagnostic process.

Logistic Regression and Artificial Neural Network: A Comparative Study in Diagnosing Breast Cancer

Breast tumor is a common problem in gynecology. A reliable test for preoperative discrimination between benign and malignant breast tumor is highly helpful for clinicians in culling the malignant cells through felicitous treatment for patients. This paper is carried out to generate and estimate both logistic regression technique and Artificial Neural Network (ANN) technique to predict the malignancy of breast tumor, utilizing Wisconsin Diagnosis Breast Cancer Database (WDBC). Our aim in this Paper is: (i) to compare the diagnostic performance of both methods in distinguishing between malignant and benign patterns, (ii) to truncate the number of benign cases sent for biopsy utilizing the best model as an auxiliary implement, and (iii) to authenticate the capability of each model to recognize incipient cases as an expert system.

Support Vector Machine Classifier for Prediction of Breast Malignancy using Wisconsin Breast Cancer Dataset

Cancer is the world's second largest cause of death. In 2018, 9.6 million people died from cancer. In any medical sickness, breast cancer is one of the most delicate and endemic diseases. This is one of the primary causes of female death in the world. Breast cancer kills one out of every eleven women around the world. "Early detection equals improved odds of survival," says a well-known cancer adage. As a result, early detection is essential for successfully preventing breast cancer and lowering morality. Breast Cancer is a type of cancer that affects one of the most significant issues that humanity has faced in recent decades has been diagnosis and prediction. Cancer detection that is accurate can save millions of lives. Effective technologies for diagnosing malignant breasts aid healthcare providers in diagnosing and treating patients in a fast and accurate manner. Experiments were carried out in this study to categorize breast cancer as benign or malignant using the Wisconsin Diagnosis Breast Cancer (WDBC) database. Support Vector Machine is a supervised learning technique (SVM). The SVM classifier's classification performance is evaluated. Experiments demonstrate that the SVM model has a fantastic performance, with a classification accuracy of 96.09 percent on the testing subset.

Diagnosis of breast cancer using machine learning algorithms based on features selected by Genetic Algorithm: Assessed on five datasets

Breast Cancer is one of the chronic diseases occurred to human beings throughout the world. Early detection of this disease is the most promising way to improve patients’ chances of survival. The strategy employed in this paper is to select the best features from various breast cancer datasets using a genetic algorithm and machine learning algorithm is applied to predict the outcomes. Two machine learning algorithms such as Support Vector Machines and Decision Tree are used along with Genetic Algorithm. The proposed work is experimented on five datasets such as Wisconsin Breast Cancer-Diagnosis Dataset, Wisconsin Breast Cancer-Original Dataset, Wisconsin Breast Cancer-Prognosis Dataset, ISPY1 Clinical trial Dataset, and Breast Cancer Dataset. The results exploit that SVM-GA achieves higher accuracy of 98.16% than DT-GA of 97.44%.

New Sequential and Parallel Support Vector Machine with Grey Wolf Optimizer for Breast Cancer Diagnosis

Breast cancer is one of the most common types of cancer worldwide. Early detection of cancer increases the probability of recovery. This work has three contributions. The first contribution is improving the performance of support vector machine (SVM) using a recent grey wolf optimizer (GWO) for diagnosis breast cancer with efficient scaling techniques. The second contribution is proposing three efficient scaling techniques against the classical normalization technique. The last contribution is using a parallel technique which applies task distribution to improve the efficiency of GWO. The proposed sequential model is applied on two different datasets, Wisconsin diagnosis breast cancer (WDBC) dataset and Electronic Health Records (EHR). Experimental results of WDBC show that the proposed hybrid GWO-SVM model achieves 98.60% with normalization scaling. Also, using the proposed scaling techniques with the proposed GWO-SVM model gives a fast convergence and achieves accuracy rate by 99.30%. The parallel version of the proposed model achieves a speedup by 3.9 on four CPU cores. On the other hand, Experimental results of EHR show that the proposed hybrid GWO-SVM model achieves 93.26% with normalization scaling against 82.05 for SVM.

From Linear Programming Approach to Metaheuristic Approach: Scaling Techniques

The objective of this work is to propose ten efficient scaling techniques for the Wisconsin Diagnosis Breast Cancer (WDBC) dataset using the support vector machine (SVM). These scaling techniques are efficient for the linear programming approach. SVM with proposed scaling techniques was applied on the WDBC dataset. The scaling techniques are, namely, arithmetic mean, de Buchet for three cases p = 1,2 , and ∞ , equilibration, geometric mean, IBM MPSX, and Lp-norm for three cases p = 1,2 , and ∞ . The experimental results show that the equilibration scaling technique overcomes the benchmark normalization scaling technique used in many commercial solvers. Finally, the experimental results also show the effectiveness of the grid search technique which gets the optimal parameters (C and gamma) for the SVM classifier.

Breast cancer classification using machine learning techniques: a comparative study

Background: The second leading deadliest disease affecting women worldwide, after lung cancer, is breast cancer. Traditional approaches for breast cancer diagnosis suffer from time consumption and some human errors in classification. To deal with this problems, many research works based on machine learning techniques are proposed. These approaches show their effectiveness in data classification in many fields, especially in healthcare. Methods: In this cross sectional study, we conducted a practical comparison between the most used machine learning algorithms in the literature. We applied kernel and linear support vector machines, random forest, decision tree, multi-layer perceptron, logistic regression, and k-nearest neighbors for breast cancer tumors classification. The used dataset is Wisconsin diagnosis Breast Cancer. Results: After comparing the machine learning algorithms efficiency, we noticed that multilayer perceptron and logistic regression gave the best results with an accuracy of 98% for breast cancer classification. Conclusion: Machine learning approaches are extensively used in medical prediction and decision support systems. This study showed that multilayer perceptron and logistic regression algorithms are performant ( good accuracy specificity and sensitivity) compared to the other evaluated algorithms.