Application of Machine Learning Algorithms in Breast Cancer Diagnosis and Classification.
Application of Machine Learning Algorithms in Breast Cancer Diagnosis and Classification.
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发表时间：
2021-01
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通讯作者：
C. Yedjou;Solange S Tchounwou;R. Aló;Rashid I. Elhag;B. Mochona;L. Latinwo
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作者：
C. Yedjou;Solange S Tchounwou;R. Aló;Rashid I. Elhag;B. Mochona;L. Latinwo
乳腺癌仍然是女性中最常见的癌症，影响约八分之一的女性，并导致全世界女性癌症相关死亡人数最多，尽管在早期诊断，筛查和患者管理方面取得了显着进展。所有的乳腺病变都不是恶性的，所有的良性病变都不会进展为癌症。然而，通过综合或术前检查，如体格检查、乳腺X线检查、细针穿刺细胞学检查和粗针穿刺活检，可以提高诊断的准确性。尽管存在一些局限性，但与单一采用的诊断程序相比，这些程序更准确，可靠和可接受。最近的研究表明，使用机器学习（ML）技术可以准确地预测和诊断乳腺癌。本研究的目的是探讨ML方法的应用，以分类乳腺癌的基础上产生的特征值从数字化图像的细针穿刺（FNA）的乳腺肿块。为了实现这一目标，我们使用ML算法，从Kaggle（https：//www.kaggle.com/uciml/breast-cancer-wisconsin-data）收集了569名乳腺癌患者的科学数据集，基于乳腺肿块FNA的十个实值特征（包括半径、纹理、周长、面积、平滑度、紧密度、曲率、凹点、对称性和分形维数）分析和解释数据。在接受测试的569名患者中，63%被诊断为良性乳腺癌，37%被诊断为恶性乳腺癌。良性肿瘤生长缓慢，不扩散，而恶性肿瘤生长迅速，并扩散到身体的其他部位。
Breast cancer continues to be the most frequent cancer in females, affecting about one in 8 women and causing the highest number of cancer-related deaths in females worldwide despite remarkable progress in early diagnosis, screening, and patient management. All breast lesions are not malignant, and all the benign lesions do not progress to cancer. However, the accuracy of diagnosis can be increased by a combination or preoperative tests such as physical examination, mammography, fine-needle aspiration cytology, and core needle biopsy. Despite some limitations, these procedures are more accurate, reliable, and acceptable, when compared with a single adopted diagnostic procedure. Recent studies have shown that breast cancer can be accurately predicted and diagnosed using machine learning (ML) technology. The objective of this study was to explore the application of ML approaches to classify breast cancer based on feature values generated from a digitized image of a fine-needle aspiration (FNA) of a breast mass. To achieve this objective, we used ML algorithms, collected a scientific dataset of 569 breast cancer patients from Kaggle (https://www.kaggle.com/uciml/breast-cancer-wisconsin-data), analyze and interpreted the data based on ten real-valued features of a breast mass FNA including the radius, texture, perimeter, area, smoothness, compactness, concavity, concave points, symmetry, and fractal dimension. Among the 569 patients tested, 63% were diagnosed with benign breast cancer and 37% were diagnosed with malignant breast cancer. Benign tumors grow slowly and do not spread while malignant tumors grow rapidly and spread to other parts of the body.