Early Breast Cancer Prediction Using Machine-Learning Algorithms

Breast cancer poses a significant global health challenge, affecting millions of women each year and leading to substantial cancer-related deaths. Early detection is crucial, particularly in regions with limited resources and inefficient healthcare systems where late-stage diagnoses are common. To tackle this issue, we propose a novel approach that employs machine-learning algorithms for early breast cancer prediction. Our model integrates a diverse ensemble of machine-learning algorithms, including K-Nearest Neighbor (KNN), Naive Bayes, Support Vector Machines (SVM), and Decision Tree Classifier. This ensemble harnesses the collective predictive power of these algorithms to distinguish between benign and malignant tumor types, enabling early diagnosis and prompt intervention. Furthermore, our approach enhances predictive capabilities by incorporating Logistic Regression, Random Forest, and additional KNN algorithms. Each algorithm contributes unique strengths to the ensemble: SVM excels in identifying complex data relationships, Logistic Regression offers interpretability, Random Forest handles large datasets and captures intricate patterns, while KNN adapts well to irregular decision boundaries. By leveraging the strengths of these diverse algorithms, our proposed system provides a more comprehensive and accurate breast cancer prediction model. Through ensemble learning, we mitigate individual algorithm weaknesses and improve predictive performance, thereby advancing the effectiveness of early breast cancer detection and ultimately improving patient outcomes. Keywords: Machine Learning, Breast Cancer Prediction, Ensemble Learning, K-Nearest Neighbor (KNN), Naive Bayes, Support Vector Machines (SVM), Decision Tree Classifier, Logistic Regression, Random Forest, Early Detection, Healthcare Systems