An ensemble learning-based framework for breast cancer prediction

Abstract

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.