Wisconsin Breast Cancer Research Articles

Breast tumor prediction and feature importance score finding using machine learning algorithms

The subject matter of this study is breast tumor prediction and feature importance score finding using machine learning algorithms. The goal of this study was to develop an accurate predictive model for identifying breast tumors and determining the importance of various features in the prediction process. The tasks undertaken included collecting and preprocessing the Wisconsin Breast Cancer original dataset (WBCD). Dividing the dataset into training and testing sets, training using machine learning algorithms such as Random Forest, Decision Tree (DT), Logistic Regression, Multi-Layer Perceptron, Gradient Boosting Classifier, Gradient Boosting Classifier (GBC), and K-Nearest Neighbors, evaluating the models using performance metrics, and calculating feature importance scores. The methods used involve data collection, preprocessing, model training, and evaluation. The outcomes showed that the Random Forest model is the most reliable predictor with 98.56 % accuracy. A total of 699 instances were found, and 461 instances were reached using data optimization methods. In addition, we ranked the top features from the dataset by feature importance scores to determine how they affect the classification models. Furthermore, it was subjected to a 10-fold cross-validation process for performance analysis and comparison. The conclusions drawn from this study highlight the effectiveness of machine learning algorithms in breast tumor prediction, achieving high accuracy and robust performance metrics. In addition, the analysis of feature importance scores provides valuable insights into the key indicators of breast cancer development. These findings contribute to the field of breast cancer diagnosis and prediction by enhancing early detection and personalized treatment strategies and improving patient outcomes.

SEMeL-LR: An improvised modeling approach using a meta-learning algorithm to classify breast cancer

In the last two decades, cancer has continued to be prone to a larger extent in females globally. In this regard, early prevention and treatment can help individuals to anticipate unnecessary deaths. At present, many advanced machine learning (ML) techniques are widely used but are still unable to diagnose breast cancer completely. Hence, in this research, a novel Stacking Ensemble Meta-Learning (SEMeL-LR) technique has been proposed that combines the predicted results of base ML models to get the best outcome with five different phases: (i) Collect Wisconsin Breast Cancer Database (WBCD) from the Kaggle repository, (ii) training original data at primary level (i.e. L0), (iii) predict with ML models at L0, (iv) build MeL model, and (v) final prediction using MeL at L1. First off, this study provides a significant contribution toward a deeper comprehension of the MeL technique, and secondly, is to compare our proposed model with five more common ML models to find the best predictive model. The experiment was carried out using Python 3.8.8 programming language on the Jupyter Notebook 6.4.3 platform, where we found the proposed model resulted in an accuracy of 98.59%, precision of 99.00%, recall of 99.00%, f1-score of 98.00%, true positive rates of 99.00%, and false positive rates of 1.00%, which was better than many well-known state-of-the-art ML models. Furthermore, we have implemented GridSearchCV () method for parameter tuning that takes 4 attributes with multiple values inside ‘params’ and found the best parameters as ‘penalty = [11, 12]’, ‘solver = ‘liblinear’, ‘c = 1438.45’ and max_iter = 100′ when grid.best_params_ had called. In the end, we tried with new sample values and found our proposed model had successfully classified the class as ‘1’, which represents a patient affected with breast cancer disease.

A study on the crucial indicators for breast cancer detection using machine learning algorithm

Since breast cancer is the most serious disease affecting women, early detection comes as a priority. The Wisconsin Breast Cancer Dataset (WBCD), which was retrieved from the UCI database, has been applied in numerous studies in recent years to help with the definitive diagnosis. Machine learning (ML) algorithms, such as K-Nearest Neighbor (KNN), Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), Naive Bayes (NB), Support Vector Machine (SVM), and Neural Network (NN) can be used to attain the upshot. Although these algorithms make predictions well, advantages cannot overshadow drawbacks because the outcome is circumstantial by the peculiar dataset itself and cannot draw a direct conclusion reflecting the deeper issue. To implement ML skills to figure out the factors that influence the prediction most in a statistical dimension, this paper uses the dataset above, compares five methods, and chooses three best classifiers: KNN, RF, and SVM. After selection, the author eliminates every single variable each time to get the accuracy, and compares them with the full model’s accuracy. Having controlled variables, it can be informed that Clump Thickness and Bare Nuclei are the factors that matter most.

An Analysis of the k-Nearest Neighbor Classifier to Predict Benign and Malignant Breast Cancer Tumors

Because of Breast Cancer's high mortality rate and being a leading cause of death among women worldwide, there has been importance given to machine learning (ML) algorithms to detect early signs of benign and malignant tumors effectively. Assistance from ML classifiers allows for a more efficient evaluation of mammographic results, surpassing the capabilities of radiologists who manually classify extensive patient data. This study aims to evaluate the effectiveness of the k-Nearest Neighbor (kNN) classifier in characterizing cancer tumor stages based on concavity, texture, area, perimeter, and smoothness. We employ scatterplots to differentiate between benign and malignant classes using the Breast Cancer Wisconsin Dataset (WBCD) from the University of California at Irvine Machine Learning Repository. Employing the k-Fold Cross Validation (k-FCV) technique, we determine the optimal value for k to assign anonymous data to their respective categories. The analysis conducted in this study finds that the most favorable value for the hyperparameter k is 12, resulting in a highly effective diagnostic outcome from administering four distinct tests. Given the absence of a predefined value for the k parameter, guesswork could lead to accuracy errors and misdiagnosis; therefore, employing k-FCV provides a more precise approach to determining the optimal class for unknown tumor attributes. Additionally, preprocessing of this dataset and measuring how different data splits impact accuracy are used to organize the data effectively and achieve reliable results. Recognizing that early detection is essential in preventing Breast Cancer-related deaths, ML techniques like kNN can greatly reduce mortality rates associated with the disease.

An ensemble learning-based framework for breast cancer prediction

Breast cancer is a disease characterised by abnormal cell growth in the breast, with various types and unique characteristics depending on the malignant cells involved. It predominantly affects women, and researchers in the field of clinical sciences are increasingly interested in leveraging Artificial Intelligence (AI) techniques to predict and diagnose breast cancer. This study utilises the widely-used Wisconsin Breast Cancer Dataset (WBCD) from the University of California Irvine (UCI) machine learning repository. The dataset contains 30 features, including mean, standard error, and “worst” values, providing insights into different aspects of breast cancer. A stacked ensemble classifier is proposed to evaluate the effectiveness of the proposed model, incorporating multiple machine learning algorithms such as the Decision Tree Classifier, AdaBoost Classifier, Gaussian Naive Bayes (GaussianNB), and Multi-layer Perceptron Classifier (MLPClassifier). Various performance measures, including the Receiver Operating Characteristic Curve (ROC curve), Area Under the Curve (AUC), specificity, F1-score, sensitivity, and accuracy, are used to assess the model’s performance. The results demonstrate that the proposed ensemble technique achieves an accuracy of 97.66%, surpassing the performance reported in existing literature. This highlights the superior performance of the developed model compared to previous approaches. The study underscores the potential of AI techniques and machine learning algorithms in advancing breast cancer prediction and diagnosis, offering promising avenues for improving clinical decision-making and patient outcomes.

Data Mining Technique for Breast Cancer Prediction using Fuzzy Theory

In order to find a reliable approach of breast cancer prediction, Data mining methods are used in the studies provided in this article. This study compares multiple patient clinical data in order to find a reliable model that can predict the occurrence of breast cancer. In this article, the support vector machine (SVM), artificial neural network (ANN), naive bayes classifier, and AdaBoost tree are used as four data mining methods. Furthermore, since it has such a significant impact on the efficacy and efficiency of the learning process, feature space is extensively examined in this work. Combining PCA with other data mining algorithms that use a PCA-like technique to compress the feature space is recommended. This hybrid is intended to assess the effect of feature space reduction. Wisconsin Breast Cancer Database (1991) and Wisconsin Diagnostic Breast Cancer (1995) are two frequently used test data sets that are used to assess the effectiveness of these algorithms. To calculate each model's test error, the method of 10-fold cross-validation is used. The findings of this research show a thorough trade-off between these tactics and also provide a thorough assessment of the models. In practical applications, it is anticipated that feature identification results would help to avoid breast cancer for both doctors and patients.

RAU: Novel Activation Function for Deep Learning Neural Network

In Deep learning neural networks (DNNs) activation functions perform a vital role. In each neuron activation function is responsible for generating output signals from given input signals. Hence, activation function is one of the factors that influence the performance of DNN. A novel activation unit RAU (Reciprocal activation unit) is proposed in this paper. Most of the popular algorithms given more importance to positive signals, but proposed method handles the negative and positive inputs equally. The proposed RAU tested with both multiclassification and binary classification datasets. Iris flower and Wisconsin Breast Cancer datasets are used for the analysis. In Breast cancer dataset RAU provides 99.25% and 97.08% accuracy for classification of train and test sets respectively. In Iris dataset RAU provides 99.05% and 97.78% accuracy for the classification of train and test sets. Analysis of the same datasets are performed with the existing activation functions- Sigmoid, RMAF, Swish, Tanh and ReLU. Results showed that RAU performed better than other activation functions.

Design of Primal Soft-margin SVM Leveraging Piecewise Linear Approximation based Linear Programming Technique

This research introduces a new approach for training soft-margin Support Vector Machines (SVMs) using the primal formulation. The method, called soft-margin Piecewise Linear Approximation based SVM (Soft-margin PLA-SVM), streamlining the optimization of soft-margin SVM hyperparameters in linear programming framework using well-known GUROBI Optimizer Solver. It eliminates the need for an initial hyperparameter guess and uses an adaptable initial search domain. The study uses the Wisconsin Breast Cancer Original dataset from UCI machine learning to validate the effectiveness of proposed soft-margin PLA-SVM. Comparative analysis shows that proposed PLA-SVM outperforms other classifiers in terms of training speed, accuracy, precision, and ROC-AUC scores. The scalability and computational efficiency of soft-margin PLA-SVM make it suitable for high-dimensional and large-scale datasets. The research demonstrates the effectiveness of the primal perspective in solving the soft-margin SVM design problem.

Implementation of SMOTE and whale optimization algorithm on breast cancer classification using backpropagation

Breast cancer, which is characterized by uncontrolled cell growth, is the primary cause of mortality among women worldwide. The unchecked proliferation of cells leads to the formation of a mass or tumor. Generally, the absence of timely and efficient treatment contributes to this phenomenon. To prevent breast cancer, one of the strategies involves the classification of malignant and non-malignant types. For this particular investigation, the Breast Cancer Wisconsin dataset (original) comprising 699 instances with 11 classes and 1 target attribute was utilized. Synthetic Minority Oversampling (SMOTE) was employed to balance the dataset, with the Backpropagation classification algorithm and the Whale Optimization Algorithm (WOA) serving as optimization techniques. The main objectives of this study were to analyze the impact of the backpropagation method and SMOTE, examine the effect of the backpropagation method in conjunction with WOA, and assess the outcome of using the backpropagation method and SMOTE after incorporating WOA. The evaluation of the study's findings was performed using a confusion matrix and the Area Under the Curve (AUC) metric. The research outcomes based on the application of backpropagation yielded an accuracy rate of 96%, precision of 94%, recall of 95%, and an AUC of 96%. Subsequently, upon implementing SMOTE and WOA, the performance of the backpropagation method improved, resulting in an accuracy rate of 99%, precision of 97%, recall of 97%, and an AUC of 98%. This notable enhancement in performance suggests that the utilization of SMOTE and WOA effectively enhances accuracy. However, it is important to note that the observed improvements are relatively modest in nature.

Performance Analysis of Accuracy of Machine Learning Classifier in the Classification of Breast Cancer Disease

The Breast cancer is recognized as a highly prevalent and life-threatening condition affecting women. In various domains, ML techniques have shown to be reliable predictors. In this study, medical data from Wisconsin breast cancer dataset were used to examine and compare supervised machine learning algorithms for predicting Breast diseases. There are several classifiers with varying level of accuracy, which is the research challenge. This paper suggests a method for enhancing the efficiency and precision of four distinct classifiers: Random Forest Tree, K- nearest neighbor (KNN), Logistic Regression, and SVM. To assess the efficacy of different algorithms, the evaluation employed both the AUC score and confusion matrix. The decision to utilize an algorithm is determined by an AUC score exceeding 0.5, which serves as an assessment metric validating the algorithm's efficacy. The employment of a machine learning technique hinges on achieving this threshold AUC score. Among all the machine learning algorithms tested, the Random forest tree attained the highest AUC score in the trial, reaching 1.0.

Random logistic machine (RLM): Transforming statistical models into machine learning approach

Data science is booming with big data, and machine learning provides better predictive analyses. However, conventional statistical models can effortlessly interpret the effect estimates, and the prediction models are generally in a closed form. Therefore, this research integrates the logistic regression model’s core with the Random Forest structure to create a blended novel machine learning method, the Random Logistic Machine (RLM). In this way, the new approach preserves the statistical and machine learning advantages. Computer simulation studies examined the predictive ability of RLM, random forest, and Logistic Regression under various scenarios. The results showed that the RLM delivers a comparable performance to Random Forests and Logistic Regression. An application to the Breast Cancer Wisconsin (Diagnostic) Data Set also demonstrates the superior performance of the new approach.

Comparision of Different Classifiers for Prediction of Breast Cancer

The cell formed in the breast are known as breast cancer. It occurs mainly in women and it may occur rarely in men also. It is considered as the most common ailment that can lead to large number of death in females every year. In spite of the factuality that cancer is treatable and can be relieve if treated at its early stages; many patients are screened for cancer only at a very late stage. Data mining technique such as classifications provides an efficient technique to classify data, where these methods are commonly used for diagnostic decision making. The Machine learning techniques propound various methods such as statistical and probabilistic methods which allow system to learn from past experiences to distinguish and identify patterns from a standard dataset. The research work presents a review of machine learning techniques which can be used in breast cancer disease detection by applying algorithms on breast cancer Wisconsin data set. Algorithms such as Navies Bayes, Random Forest, Support Vector Machine, Adaboost and Decision Trees were used. The result outcome shows that Random Forest performs better than other techniques.

Imbalanced data classification using improved synthetic minority over-sampling technique

In data mining, deep learning and machine learning models face class imbalance problems, which result in a lower detection rate for minority class samples. An improved Synthetic Minority Over-sampling Technique (SMOTE) is introduced for effective imbalanced data classification. After collecting the raw data from PIMA, Yeast, E.coli, and Breast cancer Wisconsin databases, the pre-processing is performed using min-max normalization, cleaning, integration, and data transformation techniques to achieve data with better uniqueness, consistency, completeness and validity. An improved SMOTE algorithm is applied to the pre-processed data for proper data distribution, and then the properly distributed data is fed to the machine learning classifiers: Support Vector Machine (SVM), Random Forest, and Decision Tree for data classification. Experimental examination confirmed that the improved SMOTE algorithm with random forest attained significant classification results with Area under Curve (AUC) of 94.30%, 91%, 96.40%, and 99.40% on the PIMA, Yeast, E.coli, and Breast cancer Wisconsin databases.

Comparison of Machine Learning and Deep Learning Algorithms for Classification of Breast Cancer

Statistical data from the American Cancer Society which shows that breast cancer ranks first with the highest number of cases of all types of cases of malignant tumors (cancer) worldwide. through a data mining process that is used to extract information and data analysis, a classification process can be carried out to carry out further analysis of the pattern of a data. The dataset used in this study is the Breast Cancer Wisconsin (Diagnostic) Dataset obtained from UCI Machine Learning. The purpose of this study is to compare five algorithms, namely Logistic Regression, K Neighbors Classifier (KNN), Decision Tree Classifier, Deep Neural Network, Genetic Algorithm. The results showed that deep neural network algorithms and multilayer perceptron-genetic algorithms get 96% accuracy, logistic regression algorithms have 96% accuracy, then KNN with 94%, and decision tree classifier with 92%.

Toward improving the performance of learning by joining feature selection and ensemble classification techniques: an application for cancer diagnosis.

Breast cancer is known as the most common type of cancer in women, and this has raised the importance of its diagnosis in medical science as one of the most important issues. In addition to reducing costs, the diagnosis of benign or malignant breast cancer is very important in determining the treatment method. The purpose of this paper is to present a model based on data mining techniques including feature selection and ensemble classification that can accurately predict breast cancer patients in the early stages. The proposed breast cancer detection model is developed by joining Adaptive Differential Evolution (ADE) algorithm for feature selection and Learning Vector Quantization (LVQ) neural network for classification. Our proposed model as ADE-LVQ has the ability to automatically and quickly diagnose breast cancer patients into two classes, benign and malignant. As a new evolutionary approach, ADE performs optimal configuration for LVQ neural network in addition to selecting effective features from breast cancer data. Meanwhile, we configure an ensemble classification technique based on LVQ, which significantly improves the prediction performance. ADE-LVQ has been analyzed from different perspectives on different datasets from Wisconsin breast cancer database. We apply different approaches to handle missing values and improve data quality on this database. The results of the simulations showed that the ADE-LVQ model is more successful than the equivalent and state-of-the-art models in diagnosing breast cancer patients. Also, ADE-LVQ provides better performance with less complexity, considering feature selection and ensemble learning. In particular, ADE-LVQ improves accuracy (up to 3.4%) and runtime (up to 2.3%) on average compared to the existing best method. Combined methods based on data mining techniques for breast cancer diagnosis can help doctors in making better decisions for disease treatment.

Role of transfer functions in PSO to select diagnostic attributes for chronic disease prediction: An experimental study

Particle Swarm Optimization (PSO) is a classic and popularly used meta-heuristic algorithm in many real-life optimization problems due to its less computational complexity and simplicity. The binary version of PSO, known as BPSO, is used to solve binary optimization problems, such as feature selection. Like other meta-heuristic optimization techniques designed on the continuous search space, PSO uses the transfer functions (TFs) to map the candidate solutions to the discrete search space in BPSO, and these TFs play a vital role to get the desired results. Over the years, many forms of TFs have been introduced in the literature, most of which fall under one of the five families - Linear, S-shaped, V-shaped, U-shaped, and Time-varying Mirrored S-shaped TFs. The goal of this study is to determine an appropriate setup constituting a TF and a classifier for feature selection from different types of clinical data. In this study, the impacts of the five TF families have been investigated, considering one from each family for the selection of attributes/features, while predicting disease using diagnosis or medical reports. The classification tasks are carried out using four standard classifiers: Support Vector Machine, Decision Tree, K-Nearest Neighbors, and Gaussian Naive Bayes. For experimental purposes, we have used four publicly available datasets namely, the UCI Heart Disease dataset, Wisconsin Breast Cancer dataset, UCI Chronic Kidney Disease dataset, and PIMA Indians Diabetes dataset. After an exhaustive set of experiments, we have obtained 96.72%, 99.82%, 100.00%, and 84.41% disease prediction scores in the best case for Heart disease, Breast Cancer, Chronic Kidney disease, and Diabetes, respectively. The obtained results are comparable to several state-of-the-art methods considered here for comparison. The present study helps in selecting a suitable BPSO setup (i.e., a TF and a classifier) to select important diagnostic attributes useful to design a computer-aided decision support system for the said diseases.

Small samples-oriented intrinsically explainable machine learning using Variational Bayesian Logistic Regression: An intensive care unit readmission prediction case for liver transplantation patients

Recently, machine learning has revolutionized medical diagnosis and prediction, rivaling human experts in accuracy. However, the limited interpretability poses challenges in meeting stringent medical decision standards. To facilitate interpretable predictive inference, this article proposes a small samples-oriented intrinsically explainable machine learning model, an Optimal Variational Bayesian Logistic Regression (OVBLR) model with a salient feature estimation strategy (OVBLR-SFE). OVBLR-SFE sequentially learns from limited labeled samples, mining disease-related risk factors to provide intrinsic interpretability to its predictive inference automatically. The model achieves this by reasonably approximating the posterior probability through the proposed OVBLR model to estimate the feature weight and influential effect on model outputs to facilitate salient feature selection. The effectiveness and superiority of OVBLR-SFE are demonstrated through extensive experiments on four benchmark medical datasets from the UCI repository, including the Bone Marrow Transplant: Children Data Set, Breast Cancer Wisconsin (Original) Data Set, SPECT Heart Data Set, and Heart Disease Data Set, as well as a real-world case of intensive care unit readmission prediction. Experimental results reveal that the OVBLR-SFE can achieve high accuracies of 92.02%, 97.21%, 86.71%, and 84.47%, respectively, with an average classification accuracy of 90.10% stably on the four UCI datasets. Especially, OVBLR-SFE can accurately infer significant features that align with that obtained by the post-hoc interpretation approaches: SHAP and AcME, demonstrating remarkable interpretability. In the real application, the proposed method achieved a readmission prediction accuracy of 88.11% in predicting the readmission probabilities of liver transplantation patients. Notably, the inferred salient features, such as total Intensive Care Unit length of stay (ICUTime), N-terminal pro-brain natriuretic peptide(NTproBNP), Operating time of surgery(OperationTime_h), and intraoperative red blood cell transfusion (RBCT), emerges as crucial in readmission prediction, aligning with the assessments of clinical experts. The source code of this study can be accessed at https://github.com/xqw42/OVBLR-SFE.

A two-stage feature selection approach using hybrid quasi-opposition self-adaptive coati optimization algorithm for breast cancer classification

Breast cancer (BC) is one of the leading causes of high mortality rates among women. An early disease diagnosis is crucial in breast cancer’s treatment for improving the survival rate of people who have been afflicted. Human error, incorrect diagnoses, and insufficient time make manual techniques for breast cancer diagnosis ineffective and inefficient. An automated breast cancer diagnosis system assists the medical expert in diagnosing the cancer at its earliest stage. When attempting to investigate all aspects (high dimensionality) of the medical records, the automated diagnosis system faces significant challenges. It reduces the success rate of the diagnosis system. This study presents a two-stage feature subset selection approach using quasi-opposition self-adaptive coati optimization algorithm for breast cancer classification. The proposed scheme’s fitness function aims to maximize the success rate and minimize the feature selection ratio. We used the three benchmark datasets (Breast Cancer Wisconsin Diagnostic Dataset (WDBC), Breast Cancer Wisconsin Prognostic Dataset (WPBC), and Breast Cancer Wisconsin Original Dataset (WBCD)) to examine the proposed scheme’s performance. The comparison analysis results show that the proposed scheme outperforms the other competitor schemes.

Effective full connection neural network updating using a quantized full FORCE algorithm

This paper presents a new training algorithm that can update the situation of layers’ network, and therefore, connections, neurons, and firing rate of neurons based on FORCE (first-order reduced and controlled error) training algorithm. The Quantized Full FORCE algorithm (QFF) also updates the number of neurons and connections between different layers in the network per iteration in a way that the whole firing rate of each layer is updated via selecting the best neurons and combining strong features. The update method is sequential, so that with each instance passing through the network, the network structure is updated with the Full FORCE algorithm. The algorithm updates the structure of networks with a multiple/single middle layer of the supervised version of feed forward networks such as Multilayer perceptron (MLP), changing them into partially-connected networks. A combination of principal component analysis PCA and Linear Discriminant Analysis (LDA) algorithms has been used to cluster the network input features. The paper focuses on the deep supervised MLP network with backpropagation (BP) and various datasets and its comparison with other MLP based stat of art methods and hybrid evolutionary algorithms. We achieved 98.15 percent accuracy for facial expression 98.6 and 97.7 percent for Wisconsin breast Cancer and Iris Flower in respectively. The training algorithm employed in the study enjoys a lower computational complexity while yielding faster and more accurate convergence, starting with a very low level of errors of 0.009 in comparison with the full connection network and it solves the challenge of getting stuck in local minima and poor convergence of Gradient Decent with BP.

A breast cancer risk predication and classification model with ensemble learning and big data fusion

Breast cancer is a major health issue for women all over the world. Effective care and better patient outcomes depend on early identification and precise risk prediction. Ensemble learning algorithms and merging large data have recently emerged as potential ways to predict and classify breast cancer risk. This paper aims to investigate the feasibility of using ensemble learning and big data fusion for risk prediction and categorization of breast cancer. The key focus is building a reliable and precise model by combining the best features of various learning algorithms and a variety of vast and varied data sets. This research presents an Improved XGBoost Ensembling (I-XGBoost) technique with big data analytics to predict and diagnose breast cancer cells. The proposed work considers three critical identification phases to achieve greater accuracy: data pre-processing, feature extraction, and target role. The Wisconsin breast cancer diagnostic data was used for testing the proposed method. It is also compared in terms of performance with other classification methods like Decision Tree, Random Forest, Naive Bayes, K-Nearest Neighbors, Support Vector Machines, Adaboost, and XGBoost. This research aims to determine which characteristics are most beneficial in predicting cancer as malignant or benign. We show that I-XGBoost has an impressively high accuracy score of 99.84% using Spark’s Python Application Programming Interface (API). This research emphasizes the promise of ensemble learning and big data fusion for predicting and classifying breast cancer risks. By proposing a new method that draws on the best features of several models and uses the wealth of information available in big data, it adds to the current body of knowledge. Positive findings highlight the practical use of the suggested paradigm in clinical contexts.