Wisconsin Breast Cancer Dataset Research Articles

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.

Optimized Stacking Ensemble Learning Model for Breast Cancer Detection and Classification Using Machine Learning

Breast cancer is the most frequently encountered medical hazard for women in their forties, affecting one in every eight women. It is the greatest cause of death worldwide, and early detection and diagnosis of the disease are extremely challenging. Breast cancer currently exceeds all other female cancers, including ovarian cancer. Researchers can use access to healthcare records to find previously unknown healthcare trends. According to the National Cancer Institute (NCI), breast cancer mortality rates can be lowered if the disease is detected early. The novelty of our work is to develop an optimized stacking ensemble learning (OSEL) model capable of early breast cancer prediction. A dataset from the University of California, Irvine repository was used, and comparisons to modern classifier models were undertaken. The implementation analyses reveal the unique approach’s efficacy and superiority when compared to existing contemporary categorization models (AdaBoostM1, gradient boosting, stochastic gradient boosting, CatBoost, and XGBoost). In every classification task, predictive models may be used to predict the class level, and the current research explores a range of predictive models. It is better to integrate multiple classification algorithms to generate a set of prediction models capable of predicting each class level with 91–99% accuracy. On the breast cancer Wisconsin dataset, the suggested OSEL model attained a maximum accuracy of 99.45%, much higher than any single classifier. Thus, the study helps healthcare professionals find breast cancer and prevent it from happening.

A Review on Breast Cancer Prediction Using Machine Learning

One of the most in demand research topic in today’s technology world is medical area and cancer is one of them. The second main cause of death in the world is cancer. In 2015 about 8.8 million people have died due to cancer [5]. For the early detection of breast cancer several types of research have been done to start the treatment and increase the survivability. Breast cancer affects the women mentally as well as emotionally. The goal of this challenge is to provide a framework that uses a cancer medical dataset as input and then analyses the dataset to produce findings that help medical experts better understand the state of the disease. The majority of studies concentrate on mammography results. However, incorrect detection in mammography pictures can occasionally result, endangering the patient's health. Cancer that forms in the cells of breast is said to be breast cancer. The cancer should be cured if it is diagnosed in early stage. So, with the help of machine learning algorithm the cancer should be diagnosed early. Most of the women lives are affected by the breast cancer in all over the world. There are two types of tumors that can be found in breast cancer i.e. malignant or benign. If a person is having a cancer disease, then it will be categorized as malignant otherwise it is known as benign. In 2020, 685000 deaths and 2.3 million women diagnosed with breast cancer globally, somewhere in world in every 14 seconds, a woman is diagnosed with breast cancer [1]. Patient life from the breast cancer can be saved only if it is found in early stage; if it is diagnosed later then the chances of survival are less. If the cancer is diagnosed early then the patient will get a better treatment. This study will concentrate on a few machine learning methods for identifying if a breast cancer is malignant or benign. The Wisconsin Breast Cancer Dataset, which was acquired via Kaggle, was used in this study. Our goal is to evaluate how accurately various machine learning algorithms can detect breast cancer. These include Random Forest Classifier, Decision Tree Classifier, Support Vector Machine, and K-Nearest Neighbors. All the experiments are conducted on a Jupiter platform. After the analyzing the accuracy of each algorithm the most suitable one is Support Vector Machine that gives the better accuracy among all i.e., 98%.

Imbalanced medical disease dataset classification using enhanced generative adversarial network

In general, the imbalanced dataset is a major issue in health applications. The medical data classification faces the imbalanced count of data samples, here at least one class forms only a very small minority of the data, but it is a drawback of most of the machine learning algorithms. The medical datasets are mostly imbalanced in its class labels. When the dataset is imbalanced, the existing classification algorithms typically perform badly on minority class cases. To deal the class imbalance issue, an enhanced generative adversarial network (E-GAN) is proposed in this article. The proposed approach is the consolidation of deep convolutional generative adversarial network and modified convolutional neural network (DCG-MCNN). Initially, the imbalanced data is converted into balanced data in pre-processing process. Data preprocessing comprise of data cleaning, data normalization, data transformation and data reduction using Radius Synthetic minority oversampling technique (RSMOTE) method. The DCG is considered for balancing the dataset generating extra samples under training dataset. This training dataset based, the medical disease classification is enhanced by modified CNN diagnosis model. The proposed system performed is executed in MATLAB. The performance analysis is implemented under the Breast Cancer Wisconsin Dataset that provides the higher maximum geometry mean (MGM) of 8.686, 2.931 and 5.413%, and higher Matthews’s correlation coefficient (MCC) of 9.776, 1.841 and 5.413% compared to the existing methods.

Comparison of Classification Success Rates of Different Machine Learning Algorithms in the Diagnosis of Breast Cancer.

To identify which Machine Learning (ML) algorithms are the most successful in predicting and diagnosing breast cancer according to accuracy rates. The "College of Wisconsin Breast Cancer Dataset", which consists of 569 data and 30 features, was classified using Support Vector Machine (SVM), Naive Bayes (NB), Random Forest (RF), Decision Tree (DT), K-Nearest Neighbor (KNN), Logistic Regression (LR), Multilayer Perceptron (MLP), Linear Discriminant Analysis (LDA), XgBoost (XGB), Ada-Boost (ABC) and Gradient Boosting (GBC) ML algorithms. Before the classification process, the dataset was preprocessed. Sensitivity, accuracy, and definiteness metrics were used to measure the success of the methods. Compared to other ML algorithms used in the study, the GBC ML algorithm was found to be the most successful method in the classification of tumors with an accuracy of 99.12%. The XGB ML algorithm was found to be the lowest method with an accuracy rate of 88.10%. In addition, it was determined that the general accuracy rates of the 11 ML algorithms used in the study varied between 88-95%. When the results obtained from the ML classifiers used in the study are evaluated, the efficiency of the GBC algorithm in the classification of tumors is obvious. It can be said that the success rates obtained from 11 different ML algorithms used in the study are valuable in terms of being used to predict different cancer types.

Machine Learning Methods for Breast Cancer Diagnosis

As many modern diseases begin to surface especially as of late, such as the Ebola and COVID-19 epidemics, scientists have begun developing new and innovative tactics to combat them. While new medicine and vaccines may be developed, one area that needs special attention is the diagnosis of diseases – this is because without a proper and speedy diagnosis, scientists wouldn’t be able to detect diseases, rendering treatment ineffective. Scientists have begun using machine learning algorithms to help ensure an accurate and speedy diagnosis. One specific disease that has seen frequent testing around machine learning diagnosis is breast cancer. Breast cancer is one of the deadliest and common cancers around the world for women, and due to its effects, the doctrine of speed in diagnosis is essential. This study will attempt to find out, out of three machine learning algorithms (neural networks, logistic regression and K-nearest neighbours), which one is the most effective at diagnosing breast cancer using the Wisconsin Breast Cancer Dataset. Results suggest that neural networks perform the best in diagnosing breast cancer, however only by a small margin compared to other results.

Breast Cancer Prediction Using Artificial Neural Networks Back Propagation Method

Research on breast cancer has been widely conducted and previously studied with various methods or algorithms to categorize it into benign and malignant groups. In ANN algorithm, one method called back propagation network is utilized to solve complex problems related to identification, pattern recognition prediction, and so forth. The objective of the present study is to investigate the level of accuracy and performance by ANN back propagation in predicting breast cancer. Several stages for this study are formulating the problem, collecting and processing the Wisconsin breast cancer dataset from the Kaggle site. Designing and creating an ANN algorithm system to classify cancer into malignant and benign, then examining the system to perceive the prediction accuracy, and conclude it. The results of the numerical simulation indicate that the created system of MATLAB R2016a software obtained an accuracy of 96.929% with an error of 3.071% by a combination of training parameters with epoch 1000, learning rate 0.01, goal 0.001, and hidden layer 5.

Performance Evaluation of Data Mining Classification Algorithms for Predicting Breast Cancer

The most prevalent cause of death among women is breast cancer. At an early stage, predicting breast cancer enhances the probability of a successful cure. It requires a breast cancer prediction technology capable of classifying a breast tumor as dangerous malignant or harmless benign.  This is especially true in the medical field, where classification methods are often used for finding and investigation to make decisions for the disease. This study examines the performance of six classification algorithms of data mining which are Logistic Regression classifier, Naïve Bayes classifier, Decision Tree, Random Forest Classifier, Support Vector Machine, and K-Nearest Neighbors on the Wisconsin Breast Cancer (original) dataset. The principal purpose is to measure the performance of each algorithm in terms of their accuracy, precision, sensitivity, and specificity. The findings indicate that the accuracy of Support Vector Machine has the greatest rate (97.20 %) and the lowest error rate when determining if a woman has a malignant or benign tumor.

An LDA–SVM Machine Learning Model for Breast Cancer Classification

Breast cancer is a prevalent disease that affects mostly women, and early diagnosis will expedite the treatment of this ailment. Recently, machine learning (ML) techniques have been employed in biomedical and informatics to help fight breast cancer. Extracting information from data to support the clinical diagnosis of breast cancer is a tedious and time-consuming task. The use of machine learning and feature extraction techniques has significantly changed the whole process of a breast cancer diagnosis. This research work proposed a machine learning model for the classification of breast cancer. To achieve this, a support vector machine (SVM) was employed for the classification, and linear discriminant analysis (LDA) was employed for feature extraction. We measured our model’s feature extraction performance in principal component analysis (PCA) and random forest for classification. A comparative analysis of the proposed model was performed to show the effectiveness of the feature extraction, and we computed missing values based on the classifier’s accuracy, precision, and recall. The original Wisconsin Breast Cancer dataset (WBCD) and Wisconsin Prognostic Breast Cancer dataset (WPBC) were used. We evaluated performance in two phases: In phase 1, rows containing missing values were computed using the mean, and in phase 2, rows containing missing values were computed using the median. LDA–SVM when median was used to compute missing values has better results, with accuracy of 99.2%, recall of 98.0% and precision of 98.0% on the WBCD dataset and an accuracy of 79.5%, recall of 76.0% and precision of 59.0% on the WPBC dataset. The SVM classifier had a better performance in handling classification problems when LDA was applied and the median was used as a method for computing missing values.

Gaussian Naïve Bayes Algorithm: A Reliable Technique Involved in the Assortment of the Segregation in Cancer

Cancer is a disease caused by uncontrollable cell growth. The disease is a constant subject of concern due to unavailability of treatment at a severe level. Patients who have suffered from the disease have the chance of getting saved if this fatal illness is identified in the beginning stage. The survival chance will be very low if it is detected in the final stage of cancer. As the patients could not survive in their last stage, to cure their disease, an early diagnosis is a key issue and is vital. For the classification of cancer, Gaussian Naïve Bayes is implemented in this work. By exerting it on two datasets, the algorithm is tested, in which the Wisconsin Breast Cancer Dataset (WBCD) is considered as earliest one and the next one is the Lung Cancer Dataset. The assessment result of the suggested algorithm attained 90% accuracy in the prediction of lung cancer, and in predicting breast cancer, the accuracy is 98%.

Breast Cancer Detection Using Machine Learning Techniques

Abstract: Cancer is one of the most prominent cause of fatalities around the world, accounting over 1 crore deaths in past year out of which 22.6% deaths were due to Breast cancer (BC). BC is the most common type of cancer among women, it accounts for 14.7 % of cancer cases in India. Multiple pieces of research have been conducted over the early detection of BC that can help begin treatment on time thus decreasing the mortality rate. Out of the total diagnosed, only about 86% are diagnosed correctly. Biopsy images of cells have a risk of false detection which may endanger the life of a person. There is a dire need of discovering new alternative methods that have an easy implementation with different data sets, are cost-effective, reliable, and safer, that can give an accurate prediction. This paper proposes a model combined with several Machine Learning algorithms(ML) that are Decision Trees, Artificial Neural Network, K-Nearest Neighbour, Support Vector Machine for an effective and accurate breast cancer diagnosis. Index Terms - Breast Cancer; Breast Cancer Detection; Machine Learning Key Words: Breast Cancer (BC); Artificial neural networks (ANN); Wisconsin breast cancer data set.

A Machine Learning-Based Framework for Diagnosis of Breast Cancer

Machine learning is used in the health care sector due to its ability to make predictions. Nowadays major cause of death in women is due to breast cancer. In this paper, a machine learning-based framework for the diagnosis of breast cancer has been proposed. The authors have used different feature selection methods on Breast Cancer Wisconsin (Diagnostic) dataset i.e. Chi-square, Pearson correlation between features and Feature importance. The competency of the feature selection methods has been analyzed using different machine learning classifiers on different performance parameters like accuracy, sensitivity, specificity, precision, and F-measure. Random Forest (RF), Extra Tree Classifier (ETC), and Logistic Regression (LR) machine learning classifiers have been used by the authors. Results reveal that FI (Feature Importance) is the preeminent feature selection method among all others used when applied with different classifiers. Results also show that the ETC machine learning classifier gives the best accuracy result in comparison with RF and LR classifiers.

An Improvised Random Forest Model for Breast Cancer Classification

Breast Cancer is considered as the most common cancer in females with high incidence rate. The evolution of modern facilities has helped in reducing the mortality rate, yet the incidence is still the highest among all cancers affecting women. Early diagnosis is a predominant factor for survival. Hence techniques to assist the current modalities are essential. Machine learning techniques have been used so as to produce better prediction and classification models which will aid in better and earlier disease diagnosis and classification. Random Forest is a supervised machine learning classifier that helps in better classification. Random Forests are applied to the Wisconsin breast cancer dataset and the performance of the classifier is evaluated for breast cancer classification. Here in this study an improvised random forest model which uses a cost sensitive learning approach for classification is proposed and it is found to have a better performance than the traditional random forest approach. The model gave an accuracy of 97.51%.