Breast Cancer Prediction Analysis using Machine Learning Algorithms

Vinayak A. Telsang, K. Hegde
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引用次数: 12

Abstract

Most common diseases and the leading cause of death to most women across the globe is Breast Cancer (BC). Although many individuals who suffer breast cancer have no family history but women who have blood relatives suffering from the same disease are at higher risk. Besides, a high risk of developing breast cancer includes aging, genes, thick breast tissues, obesity, and radiation exposure. Malignant and benign are two different types of tumors and to distinguish between these two, physicians need a reliable diagnostic procedure. The mammography method is used to detect breast cancer but radiologists exhibit significant variation in interpretation. Fine Needle Aspiration Cytology (FNAC) is commonly adopted in the diagnosis of breast cancer. Moreover, early diagnosis is vital to treatment with a better chance of success. Classification and data mining attributes are an efficient and effective way of categorizing results. Using machine learning models that will play a vital role in early prediction. In this paper, we present a prediction of breast cancer with different machine learning algorithms compare their prediction accuracy, area under the receiver operating characteristic curve (AUC) and performance parameters. For Simulation purposes, we are using the Wisconsin Dataset of Breast Cancer (WDBC). After analysis, the Support Vector Machine (SVM) model has achieved 96.25% accuracy with AUC of 99.4. Further, these algorithms can be modified with their mathematical models to increase the prediction of breast cancer.
使用机器学习算法进行乳腺癌预测分析
全球大多数妇女最常见的疾病和主要死亡原因是乳腺癌(BC)。虽然许多患有乳腺癌的人没有家族史,但有血缘亲属患有同样疾病的女性患病风险更高。此外,患乳腺癌的高风险因素还包括衰老、基因、乳房组织厚、肥胖和辐射暴露。恶性和良性是两种不同类型的肿瘤,为了区分这两种肿瘤,医生需要一个可靠的诊断程序。乳房x光检查方法用于检测乳腺癌,但放射科医生在解释上表现出显著的差异。细针穿刺细胞学(FNAC)是乳腺癌诊断中常用的方法。此外,早期诊断对于提高治疗成功率至关重要。分类和数据挖掘属性是对结果进行分类的一种高效的方法。使用机器学习模型将在早期预测中发挥至关重要的作用。在本文中,我们提出了一种不同的机器学习算法对乳腺癌的预测,比较了它们的预测精度、接受者工作特征曲线下面积(AUC)和性能参数。出于模拟目的,我们使用威斯康辛州乳腺癌数据集(WDBC)。经分析,支持向量机(SVM)模型准确率达到96.25%,AUC为99.4。此外,这些算法可以通过其数学模型进行修改,以提高对乳腺癌的预测。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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