A comparative analysis of machine learning algorithms for breast cancer

Abstract

With the rapid expansion of medical research in recent years, early diagnosis is now more critical than ever. A growing global population increases the risk of death from breast cancer, making it the second most severe cancer reported. For this reason, automated diagnostic systems are becoming a valuable adjunct for clinicians. This system helps in accurate diagnosis and makes it reliable, effective, and fast. By doing so, it plays a vital role in reducing the mortality associated with breast cancer. Integrating such technological advances humanizes the approach to healthcare, ensuring timely interventions that can significantly impact patient outcomes and wellbeing. As we navigate the challenges of the evolving clinical landscape, emphasizing early detection through the Action Plan underscores our commitment to the growing challenges posed by breast cancer and improving cancer healthcare delivery. Although these characteristics naturally vary from person to person, thorough testing and a wealth of clinical data combine to determine normal levels for a healthy individual. Age at survey Attempts were made to evaluate stratification strategies for quantifying the risk of individuals according to gender and specific characteristics associated with breast cancer risk. When employing machine learning with intense object pressure, various classification methods are used in the analysis, such as Support Vector Machine (SVM), Decision Tree Algorithm (DT), K-Nearest Neighbour (KNN), Logistic Regression (LR), Random Forest (RF), Naive Bayes (NB), etc. Forecasting cancer rates accurately shows progression and is notable for considering the wide range of factors that influence breast cancer risk—study methods, such as confusion matrix coefficientbased selection of features to improve model predictions further. A thorough analysis of the data, comparisons, and evaluations are made, and key performance indicators, including accuracy, precision, F-1 score, recall, sensitivity, and specificity, are reviewed, providing information.