Research Scholar, Sathyabama Institute of Science and Technology, Chennai, India.
Computer Science & Applications Department, GuruShree ShanthiVijai Jain College, Chennai, India.
J Environ Public Health. 2022 Sep 16;2022:7199290. doi: 10.1155/2022/7199290. eCollection 2022.
Breast cancer is the leading cancer in women, which accounts for millions of deaths worldwide. Early and accurate detection, prognosis, cure, and prevention of breast cancer is a major challenge to society. Hence, a precise and reliable system is vital for the classification of cancerous sequences. Machine learning classifiers contribute much to the process of early prediction and diagnosis of cancer. In this paper, a comparative study of four machine learning classifiers such as random forest, decision tree, AdaBoost, and gradient boosting is implemented for the classification of a benign and malignant tumor. To derive the most efficient machine learning model, NCBI datasets are utilized. Performance evaluation is conducted, and all four classifiers are compared based on the results. The aim of the work is to derive the most efficient machine-learning model for the diagnosis of breast cancer. It was observed that gradient boosting outperformed all other models and achieved a classification accuracy of 95.82%.
乳腺癌是女性中最常见的癌症，全球有数百万人因此死亡。早期、准确地检测、预测、治疗和预防乳腺癌是社会面临的重大挑战。因此，一个精确和可靠的系统对于癌症序列的分类至关重要。机器学习分类器在癌症的早期预测和诊断过程中做出了巨大贡献。在本文中，我们对随机森林、决策树、AdaBoost 和梯度提升等四种机器学习分类器进行了比较研究，用于良性和恶性肿瘤的分类。为了得到最有效的机器学习模型，我们利用了 NCBI 数据集。我们进行了性能评估，并根据结果比较了所有四种分类器。这项工作的目的是得到最有效的机器学习模型来诊断乳腺癌。结果表明，梯度提升在所有其他模型中表现最好，达到了 95.82%的分类准确率。
