Prediction of Breast Cancer Using Machine Learning

Breast cancer remains one of the leading causes of morbidity and mortality among women worldwide. Despite the global emphasis on early detection, breast cancer continues to pose a significant public health challenge. The object of this study is to predict the breast cancer risk using various machine-learning approaches based on demographic, laboratory, and mammographic data. It employed a quantitative research design to assess the potential of machine learning (ML) in predicting breast cancer. It integrated supervised ML algorithms, including Support Vector Machines (SVM), Decision Trees, Random Forests, and Deep Learning models, to evaluate their accuracy, efficiency, and applicability in medical diagnostics. The dataset revealed significant variability in tumor features such as mean radius, mean texture, mean perimeter, and mean area. The target variable demonstrated a class imbalance, with 62% benign and 38% malignant cases. Among the evaluated models, Random Forest outperformed others with the highest accuracy, precision, recall, F1-score, and ROC-AUC, indicating superior predictive capability. The Logistic Regression and Support Vector Machine models showed competitive performance, particularly in precision and recall, while the Decision Tree model exhibited the lowest overall performance across metrics.