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首页> 外文期刊>Journal of Big Data >Improving the performance of support-vector machine by selecting the best features by Gray Wolf algorithm to increase the accuracy of diagnosis of breast cancer
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Improving the performance of support-vector machine by selecting the best features by Gray Wolf algorithm to increase the accuracy of diagnosis of breast cancer

机译：通过使用Gray Wolf算法选择最佳特征来提高支持向量机的性能，以提高乳腺癌的诊断准确性

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Abstract One of the most common diseases among women is breast cancer, the early diagnosis of which is of paramount importance. Given the time-consuming nature of the diagnosis process of the disease, using new methods such as computer science is extremely important for early detection of the condition. Today, the main emphasis is on the science of data mining as one of the computer methods in the field of diagnosis. In the present study, we used data mining as a combination of feature selection method by Gray Wolf Optimization (GWO) and support vector machine (SVM), which is a new technique with high accuracy compared to other methods in this classification, to increase the accuracy of breast cancer diagnosis. The UCI dataset and functional parameters and various statistical criteria were applied to evaluate the proposed method and assess the validity of the results in MATLAB, respectively. Application of the proposed method increased the improvement of the evaluated criteria, which increased the accuracy of diagnosis by 27.68%, compared to former works in the field. As such, it could be concluded that the proposed method had a higher ability to diagnose breast cancer, compared to previous techniques.
机译：摘要乳腺癌是女性中最常见的疾病之一，早期诊断至关重要。鉴于疾病诊断过程的耗时性，使用新方法（例如计算机科学）对于疾病的早期检测极为重要。今天，主要重点是作为诊断领域中计算机方法之一的数据挖掘科学。在本研究中，我们将数据挖掘结合使用了灰狼优化（GWO）和支持向量机（SVM）的特征选择方法，这是与该分类中其他方法相比具有较高准确性的一项新技术，可以增加乳腺癌诊断的准确性。 UCI数据集和功能参数以及各种统计标准分别用于评估所提出的方法和在MATLAB中评估结果的有效性。与该领域以前的工作相比，该方法的应用增加了评估标准的改进，使诊断的准确性提高了27.68％。这样，可以得出结论，与先前的技术相比，所提出的方法具有更高的诊断乳腺癌的能力。

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