Author(s) :

Jagdale Swapnal Chandrakant.

Volume/Issue :

Abstract :

Breast cancer remains one of the most prevalent and life-threatening diseases affecting women worldwide, with early detection playing a critical role in reducing mortality rates. Traditional diagnostic methods such as mammography and biopsy, while effective, can be time-consuming and prone to human error. Machine learning (ML) algorithms have emerged as powerful tools in medical diagnosis, offering improved accuracy and efficiency in detecting breast cancer. This study evaluates various ML models, including Support Vector Machines (SVM), Random Forest (RF), K-Nearest Neighbors (KNN), Artificial Neural Networks (ANN), and Logistic Regression, using the Breast Cancer Wisconsin Diagnostic dataset. The research focuses on feature selection, data preprocessing, model training, and performance evaluation based on accuracy, precision, sensitivity, and specificity. Experimental results indicate that ANN achieved the highest accuracy of 97.1%, followed by RF at 96.7%, highlighting the potential of deep learning techniques in medical diagnostics. This study underscores the importance of leveraging ML-based systems in healthcare for improved diagnostic reliability and patient outcomes. Future advancements may explore hybrid models to further enhance predictive performance and clinical integration.

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