Faculty of Computer Science and Informatics, Amman Arab University, Amman, Jordan.
College of Business Administration, University of Business and Technology, Jeddah, Saudi Arabia.
Comput Intell Neurosci. 2021 Dec 21;2021:1094054. doi: 10.1155/2021/1094054. eCollection 2021.
. Breast cancer is the second greatest cause of cancer mortality among women, according to the World Health Organization (WHO), and one of the most frequent illnesses among all women today. The influence is not confined to industrialized nations but also includes emerging countries since the authors believe that increased urbanization and adoption of Western lifestyles will lead to a rise in illness prevalence. . The breast cancer has become one of the deadliest diseases that women are presently facing. However, the causes of this disease are numerous and cannot be properly established. However, there is a huge difficulty in not accurately recognizing breast cancer in its early stages or prolonging the detection process. . In this research, machine learning is a field of artificial intelligence that employs a variety of probabilistic, optimization, and statistical approaches to enable computers to learn from past data and find and recognize patterns from large or complicated groups. The advantage is particularly well suited to medical applications, particularly those involving complicated proteins and genetic measurements. . However, when using the PCA method to reduce the features, the detection accuracy dropped to 89.9%. IG-ANFIS gave us detection accuracy (98.24%) by reducing the number of variables using the "information gain" method. While the ANFIS algorithm had a detection accuracy of 59.9% without utilizing features, J48, which is one of the decision tree approaches, had a detection accuracy of 92.86% without using features extraction methods. When applying PCA techniques to minimize features, the detection accuracy was lowered to the same way (91.1%) as the Naive Bayes detection algorithm (96.4%).
乳腺癌是世界卫生组织（WHO）认定的女性第二大致癌死亡原因，也是当今所有女性中最常见的疾病之一。这种影响不仅局限于工业化国家，也包括新兴国家，因为作者认为，城市化和西方生活方式的采用将导致疾病发病率上升。
乳腺癌已经成为女性目前面临的最致命疾病之一。然而，这种疾病的病因很多，无法正确确定。然而，在早期阶段无法准确识别乳腺癌或延长检测过程存在巨大困难。
在这项研究中，机器学习是人工智能的一个领域，它采用各种概率、优化和统计方法，使计算机能够从过去的数据中学习，并从大型或复杂的群体中找到和识别模式。该优势特别适合医疗应用，尤其是涉及复杂蛋白质和遗传测量的应用。
然而，当使用 PCA 方法来减少特征时，检测精度下降到 89.9%。IG-ANFIS 通过使用“信息增益”方法减少变量数量，为我们提供了 98.24%的检测精度。虽然 ANFIS 算法在不使用特征的情况下检测精度为 59.9%，但决策树方法之一的 J48 在不使用特征提取方法的情况下检测精度为 92.86%。当应用 PCA 技术来最小化特征时，检测精度也降至与朴素贝叶斯检测算法（96.4%）相同的方式（91.1%）。
