Breast cancer: A comparative review for breast cancer detection using machine learning techniques

Breast cancer is the most common cancer among women globally and presents a significant challenge due to its rising incidence and fatality rates. Factors such as cultural, socioeconomic, and educational barriers contribute to inadequate awareness and access to healthcare services, often leading to delayed diagnoses and poor patient outcomes. Furthermore, fostering a collaborative approach among healthcare providers, policymakers, and community leaders is crucial in addressing this critical women's health issue, reducing mortality rates, alleviating, and the overall burden of breast cancer. The main goal of this review is to explore various techniques of machine learning algorithms to examine high accuracy and early detection of breast cancer for the safe health of women. Comparative analysis of machine learning approaches for breast cancer predictionEnhancing awareness and reducing the gap between patients and doctorsIdentification of testing issues related to breast cancer prediction modelsComparison of support vector machine with other machine learning techniquesUtilizing machine learning techniques to improve prediction accuracyPotential of integrating machine learning models into clinical decision support systemAnalysis of different feature selection algorithms and their effectiveness