Abd-Elnaby Muhammed, Alfonse Marco, Roushdy Mohamed
Faculty of Computers and Information Science, Ain Shams University, Cairo, Egypt.
Faculty of Computers and Information Technology, Future University, New Cairo, Egypt.
J Biomed Inform. 2021 May;117:103764. doi: 10.1016/j.jbi.2021.103764. Epub 2021 Apr 6.
Cancer, in particular breast cancer, is considered one of the most common causes of death worldwide according to the world health organization. For this reason, extensive research efforts have been done in the area of accurate and early diagnosis of cancer in order to increase the likelihood of cure. Among the available tools for diagnosing cancer, microarray technology has been proven to be effective. Microarray technology analyzes the expression level of thousands of genes simultaneously. Although the huge number of features or genes in the microarray data may seem advantageous, many of these features are irrelevant or redundant resulting in the deterioration of classification accuracy. To overcome this challenge, feature selection techniques are a mandatory preprocessing step before the classification process. In the paper, the main feature selection and classification techniques introduced in the literature for cancer (particularly breast cancer) are reviewed to improve the microarray-based classification.
根据世界卫生组织的数据，癌症，尤其是乳腺癌，被认为是全球最常见的死因之一。因此，为了提高治愈的可能性，人们在癌症的准确早期诊断领域进行了广泛的研究。在现有的癌症诊断工具中，微阵列技术已被证明是有效的。微阵列技术可以同时分析数千个基因的表达水平。虽然微阵列数据中大量的特征或基因看似有利，但其中许多特征是不相关或冗余的，这会导致分类准确率下降。为了克服这一挑战，特征选择技术是分类过程之前必不可少的预处理步骤。本文综述了文献中为癌症（特别是乳腺癌）引入的主要特征选择和分类技术，以改进基于微阵列的分类。
