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Because of the efficiency of the system and the fact that it successfully completed the tasks that were given to it under specific conditions, we are compelled to look for a way to measure these requirements according to the conditions and guidelines that were established by the people who make use of the system. Conduct an investigation into the many techniques that are available for use in analysis in light of the following conditions: i) sufficient time to detect the mistake, ii) time to maintenance, iii) the total number of constituents involved in the analytical process, and iv) an explanation of the level of complexity provided to the user. In this article, we will provide a concise overview of a number of different approaches, along with our recommendations for the most effective ones based on the issues raised earlier. [ABSTRACT FROM AUTHOR]

Unchecked breast cell growth is one of the leading causes of death in women globally and is the cause of breast cancer. The only method to avoid breast cancer-related deaths is through early detection and treatment. The proper classification of malignancies is one of the most significant challenges in the medical industry. Due to their high precision and accuracy, machine learning techniques are extensively employed for identifying and classifying various forms of cancer. The authors of this review studied numerous data mining algorithms and implemented them such that clinicians might use them to accurately detect cancer cells early on. This article introduces several techniques, including support vector machine (SVM), K star (K*) classifier, additive regression (AR), back propagation (BP) neural network, and Bagging. These algorithms are trained using a set of data that contains tumor parameters from breast cancer patients. Comparing the results, the authors found that SVM and Bagging had the highest precision and accuracy, respectively. Also assess the number of studies that provide machine learning techniques for breast cancer detection. [ABSTRACT FROM AUTHOR]