Optimized Machine Learning Models for Early Detection of Breast Cancer

Breast cancer remains the most frequently diagnosed malignancy among women and a leading cause of cancer-related mortality worldwide. Early and accurate diagnosis is essential for improving treatment outcomes and survival rates. This research explores the integration of advanced machine learning (ML) techniques in breast cancer diagnostics, using the Breast Cancer Wisconsin (Diagnostic) Dataset. Several ML models, including Logistic Regression (LR), Random Forest Classifier (RFC), Gradient Boosting Machines (GBM), Support Vector Machines (SVM), and Deep Neural Networks (DNN), were analyzed for their diagnostic performance. Feature selection through Genetic Algorithms (GA) was applied to enhance model accuracy and efficiency by optimizing data representation. Experimental results highlight significant improvements in precision, recall, and overall diagnostic accuracy across all models, with ensemble techniques such as RFC and GBM emerging as top performers. This study demonstrates the potential of ML in advancing breast cancer diagnostics, paving the way for more efficient, reliable, and scalable diagnostic solutions in clinical settings.