Wisconsin Breast Cancer Dataset Research Articles

A Breast Cancer Diagnosis Framework Based on Machine Learning

Breast cancer is becoming the leading cause of mortality among women. One of the most prevalent diseases in women, breast cancer is brought on by a variety of clinical, lifestyle, social, and economic variables. Predictive approaches based on machine learning offer methods for diagnosing breast cancer sooner. It may be found using a variety of analytical methods, including Breast MRI, X-ray, thermography, mammograms, ultrasound, etc. The most prevalent technique for performance evaluation uses accuracy measures, and the Convolutional Neural Network (CNN) is the most accurate and widely used model for breast cancer diagnosis. The Wisconsin Breast Cancer Datasets (WBCD) were used to evaluate the suggested method. Out of a total of 569 samples, 273 samples were chosen for this experiment as the test data, while the other samples were utilized for training and validation. The review's findings showed that the Convolutional Neural Network (CNN) is the most effective and widely used model for finding breast cancer, and that the most often used technique for judging performance is accuracy metrics. The application of deep learning to such a wide range of real-world issues is astounding.

BREAST CANCER DETECTION WITH MACHINE LEARNING APPROACH

One of the most widespread diseases among women today is breast cancer. Early and accurate diagnosis is key in rehabilitation and treatment. The usage of mammograms has some uncertainties in the detection rate. To develop tools for physicians for effective and early detection and diagnosis, machine learning techniques can be adopted. The introduction of Machine Learning (ML) in developing the tool will increase the survival rate of patients with breast cancer. This research work proposed different six ML techniques; Logistic Regression, Linear Discriminant Analysis, Decision Tree (DT), KNN, Naïve Bayes (NB), and Support Vector Machine (SVM), and then recommended the model with the highest accuracy for breast cancer detection. The experiment was carried out in a python environment and all the aforementioned techniques were validated with Wisconsin Breast Cancer dataset and evaluated with accuracy, precision, and recall.

Predicting Breast Cancer with Ensemble Methods on Cloud

There are many dangerous diseases and high mortality rates for women (including breast cancer). If the disease is detected early, correctly diagnosed and treated at the right time, the likelihood of illness and death is reduced. Previous disease prediction models have mainly focused on methods for building individual models. However, these predictive models do not yet have high accuracy and high generalization performance. In this paper, we focus on combining these individual models together to create a combined model, which is more generalizable than the individual models. Three ensemble techniques used in the experiment are: Bagging; Boosting and Stacking (Stacking include three models: Gradient Boost, Random Forest, Logistic Regression) to deploy and apply to breast cancer prediction problem. The experimental results show the combined model with the ensemble methods based on the Breast Cancer Wisconsin dataset; this combined model has a higher predictive performance than the commonly used individual prediction models.

Intelligent Fuzzy System to Predict the Wisconsin Breast Cancer Dataset

Decision Support Systems (DSSs) are solutions that serve decision-makers in their decision-making process. For the development of these intelligent systems, two primary components are needed: the knowledge database and the knowledge rule base. The objective of this research work was to implement and validate diverse clinical decision support systems supported by Mamdani-type fuzzy set theory using clustering and dynamic tables. The outcomes were evaluated with other works obtained from the literature to validate the suggested fuzzy systems for categorizing the Wisconsin breast cancer dataset. The fuzzy Inference Systems worked with different input features, according to the studies obtained from the literature. The outcomes confirm that most performance' metrics in several cases were greater than the achieved results from the literature for the output variable for the different Fuzzy Inference Systems-FIS, demonstrating superior precision.

A fuzzy rule‐based system with decision tree for breast cancer detection

AbstractBreast cancer is possibly the deadliest illness in the world and the risks are gradually increasing. One out of eight women has the chance to be detected with breast cancer in their lifetime. The utmost cause for the higher fatality rates is the prolonged prognosis for the detection of breast cancer. The focus of this study is therefore to develop a better fuzzy expert system for the detection of breast cancer using decision tree analysis for deriving the rule base. For this classification problem, the input features of the dataset are converted into human‐understandable terms‐linguistic variables. The Mamdani Fuzzy Rule‐Based system is deployed as the main inference engine and the centroid method for the defuzzification process to convert the final fuzzy score into class labels‐ benign (not cancerous) or malignant (cancerous). A decision tree algorithm is applied the creating a novel set of 27 fuzzy rules which are fed into FRBS. The investigation is performed on the publicly available Wisconsin Breast Cancer Dataset. The accuracy obtained by the proposed system is about 97%, recall is 99.58% and precision is about 93%. The experiments on this dataset yield higher performance as compared to the state‐of‐the‐art dataset.

On the performance and interpretability of Mamdani and Takagi-Sugeno-Kang based neuro-fuzzy systems for medical diagnosis

PurposeNeuro-fuzzy systems aim to combine the benefits of artificial neural networks and fuzzy inference systems: a neural network can learn patterns from data and achieves high performance, whereas a fuzzy system matches inputs and outputs using linguistic and interpretable rules. The combination of these two techniques yields models that can both perform well and provide interpretability in a fuzzy linguistic manner. DesignIn this paper, the performance and interpretability of five neuro-fuzzy classifiers were evaluated (three Takagi-Sugeno-Kang (TSK) classifiers: adaptive neuro-fuzzy inference system (ANFIS), dynamic evolving neuro-fuzzy system (DENFIS), self-organizing fuzzy neural network (SOFNN), and two Mamdani classifiers: hybrid fuzzy inference system (HyFIS) and neuro-fuzzy classifier (NEFCLASS)). All the empirical evaluations were over four benchmark medical datasets (Wisconsin breast cancer dataset, SPECTF heart dataset, Parkinsons dataset, and diabetic retinopathy Debrecen dataset), and used five performance criteria (accuracy, precision, recall, f score, and training time) and two interpretability criteria (number of rules and number of membership functions). FindingsResults showed that the TSK-based self-organizing fuzzy neural network classifier, in general, outperformed the others. In terms of interpretability, DENFIS and NEFCLASS were the best Takagi-Sugeno-Kang and Mamdani classifiers respectively. The findings also suggested that three classifiers: DENFIS, SOFNN, and NEFCLASS achieved a good performance-interpretability tradeoff. OriginalityTo the best of our knowledge, no study has compared the neuro-fuzzy techniques presented in this paper in terms of performance and interpretability in the medical domain.

Analysis and Comparison for Innovative Prediction Technique of Breast Cancer Tumor by Linear Discriminant Analysis Algorithm over Support Vector Machine Algorithm with Improved Accuracy

Aim: The objective of this study is to use machine learning algorithms to detect the presence of breast cancer tumors and compare accuracy, sensitivity, and precision between the Linear Discriminant Analysis (LDA) and Support vector machine algorithm (SVM). Materials and Methods: The research uses two sets of data from the Wisconsin Breast Cancer dataset, which is obtained from the UCI Machine Learning Repository. Linear Discriminant Analysis (N=20) and Support vector machine (N=20) with sample size in accordance to total sample size calculated using clincalc.com by keeping alpha error-threshold at 0.05, confidence interval at 95%, enrollment ratio as 0:1, and power at 80%. The accuracy, sensitivity, and precision are calculated using MATLAB software. Results: Comparison of accuracy (%), sensitivity (%), and precision (%) are done using SPSS software using independent sample t-test. Linear Discriminant Analysis algorithm results in an accuracy of 88.25% (p<0.001), the sensitivity of 94.68% (p<0.001), and precision of 84.35% (p<0.001). Support vector machine algorithm results in an accuracy of 97.50%, sensitivity of 95.83%, and precision of 100%. Conclusion: Support vector machine algorithm performed significantly better than Linear Discriminant Analysis algorithm with improved accuracy of 97.50% for breast cancer prediction.

Breast Cancer Classification Using Outlier Detection and Variance Inflation Factor

In terms of malignant tumors, breast cancer is one of the most prevalent. Breast cancer is a form of cancer that develops in the breast tissue when the surrounding, healthy breast tissue is overtaken by the uncontrollably growing cells in the breast tissue. Several features or patient conditions can be used in a machine learning approach to predict breast cancer. Machine learning will be utilized in these situations to determine if the cancer is malignant or benign. The Wisconsin Breast Cancer (Diagnostic) Data Set, which contains 32 characteristics and 569 collected data, was the dataset used in this research.. Feature selection in this study is done by eliminating outliers using the upper and lower quartile of each feature then feature selection is also carried out on features that have features that have a high variance inflation factor. The machine learning methods used in this research are Logistic Regression, Random Forest, KNN, SVC, XG Boost, Gradient Boosting, and Ridge Classifier. The selection of this method is based on the target that will be predicted by 2 labels, namely benign cancer, and malignant cancer. The result obtained is that the selection of features using the variance inflation factor increases the accuracy of the previous Logistic Regression and Random Forest methods from 98.25% to 99.12%. The method that has the highest level of accuracy is the Logistic Regression and Random Forest methods which have a value of 99.12%. The next research will be developed by trying other optimization techniques for hyperparameter tuning.

Multimodal prediction of breast cancer using radiogenomics and clinical trials with decision fusion

Multimodal analysis focuses on the internal and external manifestations of cancer cells to provide physicians, oncologists and surgeons with timely information on personalized diagnosis and treatment for patients. Decision fusion in multimodal analysis reduces manual intervention, and improves classification accuracy facilitating doctors to make quick decisions. Genetic characteristics extracted on biopsies do not, however, provide details on adjacent cells. Images can only provide external observable details of cancer cells. While mammograms can detect breast cancer, region wise details can be obtained from ultrasound images. Hence, different types of imaging techniques are used. Features are extracted using the SelectKbest method in the Wisconsin Breast Cancer, Clinical and gene expression datasets. The features are extracted using Gray Level Co-occurrence Matrix from Histology, Mammogram and Sonogram images. For image datasets, the Convolution Neural Network (CNN) is used as a classifier. The combined features from clinical, gene expression and image datasets are used to train an Integrated Stacking Classifier. The integrated multimodal system’s effectiveness is shown by experimental findings.

Machine learning-based diagnosis of breast cancer utilizing feature optimization technique

Breast cancer disease is recognized as one of the leading causes of death in women worldwide after lung cancer. Breast cancer refers to a malignant neoplasm that develops from breast cells. Developed and less developed countries both are suffering from this extensive cancer. This cancer can be recuperated if it is detected at an early stage. Many researchers have proposed several machine learning techniques to predict breast cancer with the highest accuracy in the past years. In this research work, the Wisconsin Breast Cancer Dataset (WBCD) has been used as a training set from the UCI machine learning repository to compare the performance of the various machine learning techniques. Different kinds of machine learning classifiers such as support vector machine (SVM), Random Forest (RF), K-nearest neighbors(K-NN), Decision tree (DT), Naïve Bayes (NB), Logistic Regression (LR), AdaBoost (AB), Gradient Boosting (GB), Multi-layer perceptron (MLP), Nearest Cluster Classifier (NCC), and voting classifier (VC) have been used for comparing and analyzing breast cancer into benign and malignant tumors. Various matrices such as error rate, Accuracy, Precision, F1-score, and recall have been implemented to measure the model's performance. Each Algorithm's accuracy has been ascertained for finding the best suitable one. Based on the analysis, the result shows that the Voting classifier has the highest accuracy, which is 98.77%, with the lowest error rate. Finally, a web page is developed using a flask micro-framework integrating the best model using react.

Breast cancer classification using a novel hybrid feature selection approach

Many women around the world die due to breast cancer. If breast cancer is treated in the early phase, mortality rates may significantly be reduced. Quite a number of approaches have been proposed to help in the early detection of breast cancer. A novel hybrid feature selection model is suggested in this study. This novel hybrid model aims to build an efficient feature selection method and successfully classify breast lesions. A combination of relief and binary Harris hawk optimization (BHHO) hybrid model is used for feature selection. Then, k-nearest neighbor (k-NN), support vector machine (SVM), logistic regression (LR) and naive Bayes (NB) methods are preferred for the classification task. The suggested hybrid model is tested by three different breast cancer datasets which are Wisconsin diagnostic breast cancer dataset (WDBC), Wisconsin breast cancer dataset (WBCD) and mammographic breast cancer dataset (MBCD). According to the experimental results, the relief and BHHO hybrid model improves the performance of all classification algorithms in all three datasets. For WDBC, relief-BHO-SVM model shows the highest classification rates with an of accuracy of 98.77%, precision of 97.17%, recall of 99.52%, F1-score of 98.33%, specificity of 99.72% and balanced accuracy of 99.62%. For WBCD, relief-BHO-SVM model achieves of accuracy of 99.28%, precision of 98.76%, recall of 99.17%, F1-score of 98.96%, specificity of 99.56% and balanced accuracy of 99.36%. Relief-BHO-SVM model performs the best with an accuracy of 97.44%, precision of 97.41%, recall of 98.26%, F1-score of 97.84%, specificity of 97.47% and balanced accuracy of 97.86% for MBCD. Furthermore, the relief-BHO-SVM model has achieved better results than other known approaches. Compared with recent studies on breast cancer classification, the suggested hybrid method has achieved quite good results.

Breast Cancer Diagnosis Using Feature Selection Approaches and Bayesian Optimization

Breast cancer seriously affects many women. If breast cancer is detected at an early stage, it may be cured. This paper proposes a novel classification model based improved machine learning algorithms for diagnosis of breast cancer at its initial stage. It has been used by combining feature selection and Bayesian optimization approaches to build improved machine learning models. Support Vector Machine, K-Nearest Neighbor, Naive Bayes, Ensemble Learning and Decision Tree approaches were used as machine learning algorithms. All experiments were tested on two different datasets, which are Wisconsin Breast Cancer Dataset (WBCD) and Mammographic Breast Cancer Dataset (MBCD). Experiments were implemented to obtain the best classification process. Relief, Least Absolute Shrinkage and Selection Operator (LASSO) and Sequential Forward Selection were used to determine the most relevant features, respectively. The machine learning models were optimized with the help of Bayesian optimization approach to obtain optimal hyperparameter values. Experimental results showed the unified feature selection-hyperparameter optimization method improved the classification performance in all machine learning algorithms. Among the various experiments, LASSO-BO-SVM showed the highest accuracy, precision, recall and F1-score for two datasets (97.95%, 98.28%, 98.28%, 98.28% for MBCD and 98.95%, 97.17%, 100%, 98.56% for MBCD), yielding outperforming results compared to recent studies.

Neutrosophic-based Machine Learning Techniques for Analysis and Diagnosis the Breast Cancer

Approximately one in eight women will get breast cancer in their lifetime. Because of the risks associated with radiation exposure, various women choose to avoid getting detected with breast cancer. Non-invasive breast cancer detection methods have limitations concerning the safety of radiation exposure and the accuracy with which tumors in the breast are diagnosed. Machine learning methods are commonly used to diagnose breast cancer. This paper applied three different machine learning methods like KNN, Naïve Bayes, and ID3. These methods are applied to the Wisconsin Breast Cancer dataset. In the process of categorization, data with unbalanced classes is problematic because methods are more probable to categorize fresh observations to the majority class since the likelihood of cases forming the plurality class is considerably high. So neutrosophic set is used to overcome the vague and uncertain data. This paper used single-valued neutrosophic numbers to evaluate the criteria. This paper used ROC and accuracy to evaluate the methods. The KNN has a 96.7%, Naïve Bayes has a 95.2%, and ID3 has a 95.3% accuracy.

Comparative Study of Different Machine Learning Classifiers Using Multiple Feature Selection Techniques for Breast Cancer Classification

Abstract: This research investigates use of several Machine Learning classifiers under feature selection methods: Without Dimensionality reduction, using Correlation Coefficient Score, using Voting Classifier, and using Tree Based Feature Selection. The different ML Classifiers used in this research are: Logistic Regression, Decision Trees, Support Vector Machine (SVM), Random Forest, K-Nearest Neighbours (KNN) and Naïve Bayes Classifier. These classification models are run on data generated from processing mammography scans to extract shape, texture, size and other spatial features from the tumour contour. The performance of these ML classifiers is evaluated by performance metrics like: Precision Score, Recall Score, F1 Score, and Accuracy Score. The dataset used for the purpose of our study was The Wisconsin Breast Cancer Dataset for both training and testing. The comparison of these results helps us better understand the nature of these classifiers for such classification problems, give us more insights on feature engineering and selection, and their potential use in clinical trials. After computing the results, we were able to get accuracy levels as high as 97.9% and were able to reach accuracy between 90- 95% in general.

Analysis of Different Machine Learning Techniques with PCA in the Diagnosis of Breast Cancer

In recent years, different types of cancer cases are common. Increasing cancer cases, A rapidly increasing health for countries and humanity becomes a problem. In addition to being the most common cancer among women today, breast cancer has surpassed lung cancer as the most common cancer type in the world since 2021. Early diagnosis greatly reduces the risk of death in breast cancer, and benign tumors are correctly diagnosed, allows the classification of this field to be a new research topic. New developments in the field of Medicine and Technology Machine learning, classification algorithms and computerized diagnosis are used in the correct classification of tumors. increased its use. These systems are extremely important in terms of being an assistant to the expert opinion. In this study, in the Wisconsin Breast Cancer dataset, it is aimed to accelerate the diagnosis of the disease and to reduce the tumors, different machine learning to minimize treatment processes by providing accurate classification techniques were used. In this study, we reduced our dataset to 171 data using Principal Component Analysis (PCA) to accelerate disease diagnosis on the Wisconsin Breast Cancer dataset and 2 different classification processes were performed using 5 different machine learning. The success rate of each algorithm was compared, and it was revealed that Logistic Regression was the most successful method with an accuracy rate of 98.8% after PCA.

Performance comparison of two non-parametric classifiers for classification using geometric features

This study aims to examine and compare the performances of Random Forest (RF) and k-Nearest Neighbor (k-NN) algorithms used for classification based on certain geometric features. For the purpose of the analysis, the Breast Cancer Wisconsin (BCW) public dataset is used. BCW dataset contains features like area, perimeter, radius, compactness, and symmetry computed from 357 benign, and 212 malignant breast images, respectively. Three different experiments related to the size of training and testing datasets for classification are conducted and different accuracy values are obtained. The best accuracy of 91.9% for RF and 91.3% for kNN, respectively, are reached when 30% of the entire dataset is used as testing dataset. For all experiments, the RF classifier outperformed the kNN.

Comparison of Decision Tree, Random Forest and Linear Discriminant Analysis Models in Breast Cancer Prediction

Breast cancer is one of the illnesses that has become a leading cause of female death worldwide, and early diagnosis of these diseases is a difficult and important undertaking. The intention of this paper is to design prediction models based on Decision Tree, Random Forest and Linear Discriminant Analysis respectively to forecast the breast cancer occurrence at an early stage. The design is focused on analyzing the minimum set of attributes from the clinical data set that have been chosen by experts, and then compare the performances of the models to find their advantages in each model. Breast Cancer Wisconsin Data Set is used in this paper to construct the expected prediction models. Classification accuracy which is determined by comparing real values with predicted values, and generalization ability which is reflected by Receiver Operating Characteristic curve, are used to gauge the performance of the models. The results confirm that the Random Forest model can achieve the highest accuracy to 99.4% and the best generalization ability. However, the Linear Discriminant Analysis model can keep better stability in prediction accuracy and has the fastest running speed.

Using a Logistic Regression and K Nearest Neighbor Model to Accurately Diagnose Breast Cancer

Breast cancer is one of the most dangerous and rapidly growing diseases in the world. Diagnosing breast cancer is expensive, difficult, and time-consuming. However, artificial intelligence and machine learning algorithms can help physicians to diagnose people with breast cancer at an early stage which will help people to avoid exhaustive treatments. The objective of our research was to classify if someone has malignant or benign cancer. We used the Wisconsin Breast Cancer dataset which was obtained from the UCI repository to create models using supervised learning. We used K Nearest Neighbors, and Logistic Regression algorithms to obtain a model with high accuracy. Both the models had an accuracy of 97%. In the future, the model can be enhanced to be more accurate and accessible to people. This research can help others to create models to predict various other cancers. In the future, we would also like to improve the model by using other methods like image recognition and reducing the input from the user to make it more accurate and accessible.

Applications of the Topological Data Analysis in Real Life

Statistical topology inference is a branch of algebraic topology that analyzes the geometric structure's global topological properties underlying a point cloud dataset. There is an increasing need to analyze massive data sets and screen large databases to address real-world problems. A central challenge to modern applied mathematics is the need to generate tools to simplify the data in high dimensional order to extract the important features or the relationships while performing the analysis. A growing field of study at the intersection of algebraic topology, computational geometry, and statistics is topological data analysis (TDA) inference. This study applies TDA tools to test hypothesis between two high-dimensional data sets. Hypothesis testing is one of the most important topics of statistical topology inference. A proposed test was created, which was built on the nearest-neighbor function. Three tests such as (Hypothesis testing based on persistent homology, hypothesis testing based on persistent landscapes, and hypothesis testing based on density estimation) based on TDA, are discussed. Moreover, a modification of these tests was proposed. Monte Carlo simulation was conducted to compare the power of the previous tests. We displayed the use of TDA tools in hypothesis testing. It was proposed that this test might be established based on the nearest neighbor distance function. Furthermore, a suggested modification for the present tests based on TDA was introduced. Finally, the tests specified in the vignette were enabled by two empirical applications within the biology field. We demonstrated the efficacy of the above tests on the heart disease dataset from Statlog and the Wisconsin breast cancer dataset.

A Linear Discriminant Analysis and Classification Model for Breast Cancer Diagnosis

Although most cases are identified at a late stage, breast cancer is the most public malignancy amongst women globally. However, mammography for the analysis of breast cancer is not routinely available at all general hospitals. Prolonging the period between detection and treatment for breast cancer may raise the likelihood of proliferating the disease. To speed up the process of diagnosing breast cancer and lower the mortality rate, a computerized method based on machine learning was created. The purpose of this investigation was to enhance the investigative accuracy of machine-learning algorithms for breast cancer diagnosis. The use of machine-learning methods will allow for the classification and prediction of cancer as either benign or malignant. This investigation applies the machine learning algorithms of random forest (RF) and the support vector machine (SVM) with the feature extraction method of linear discriminant analysis (LDA) to the Wisconsin Breast Cancer Dataset. The SVM with LDA and RF with LDA yielded accuracy results of 96.4% and 95.6% respectively. This research has useful applications in the medical field, while it enhances the efficiency and precision of a diagnostic system. Evidence from this study shows that better prediction is crucial and can benefit from machine learning methods. The results of this study have validated the use of feature extraction for breast cancer prediction when compared to the existing literature.