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AbstractBreast Cancer represents one of the disease that make a high number of deaths every year. It is the most common type of all cancers and the main cause of womens deaths worldwide. Classification and data mining methods are an effective way to classify data. Especially in medical field, where those methods are widely used in diagnosis and analysis to make decisions. In this paper, a performance comparison between different machine learning algorithms. Random forest algorithm, Support Vector Machine(SVM), Logistic Regression and Decision Tree on the Breast Cancer Wisconsin (Diagnostic) datasets is conducted. The main objective is to assess the correctness in classifying data with respect to efficiency and effectiveness of each algorithm in terms of accuracy,precision,recall and specificity. The results obtained are very competitive and can be used for predict and treatment.Keywords:Breast Cancer, Machine Learning, Classification, Accuracy, Precision,SVM. 1. IntroductionAround the world, Breast cancer is the most widely recognized type of cancer alongside lung and bronchus cancer, prostate cancer, colon cancer, and pancreatic cancer among others. Breast cancer might be a prevalent reason for death, and it's the main kind of malignant growth that is boundless among ladies in the around the world. Breast Cancer causes are multifactorial and include family ancestry, weight hormones, radiation treatment, and even reproductive factors. As indicated by the report of the world health organization every year, 2.1 million ladies are recently affected by breast cancer, and furthermore cause the highest number of can cerrelated deaths among ladies . In 2023, it is assessed that 627,000 ladies died from breast cancer - that is roughly 15% of all cancer deaths among ladies. While breast cancer growth rates are higher among ladies in extra developed areas, rates are expanding in about each locale internationally. Many imaging techniques are developed for early identification and treatment of breast cancer and to scale back the amount of death and lots of aided breast cancer diagnosis methods are wont to increase the symptomatic precision. Machine Learning algorithms are widely utilized in intelligent human services frameworks, particularly for breast cancer diagnosis and guess. There are many many machine learning classification and algorithms for prediction of breast cancer outcomes but during this paper, we are comparing various sorts of classification algorithms like Random forest algorithm, Support Vector Machine(SVM), Logistic Regression and Decision Tree. And furthermore, assess and compare the performance of the varied classifiers as far as accuracy, precision, recall, and f1-Score. The outcomes obtained during this paper provide a summary of the condition of modern Machine Learning strategies for breast cancer Prediction.

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