基于基因表达数据的集成方法在吸烟者肺癌鉴定中的应用

Otniel Abiezer., F. Nhita, I. Kurniawan
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引用次数: 1

摘要

癌症是细胞异常生长的一种症状,是无法控制的。肺癌是最常见的癌症之一。吸烟是导致肺癌的主要原因。早期发现至关重要,因为它可以预防肺癌并得到适当的治疗,例如低剂量CT扫描(LDCT)。然而,这种努力仍然有缺点。随着DNA微阵列技术的进步,可以测量每个组织中数千个基因或细胞的基因表达水平。肺癌的鉴定可以使用机器学习从基因表达数据(DNA微阵列)。在本研究中,使用集成方法,即随机森林和AdaBoost,建立了一个机器学习预测模型。最好的模型是具有900个特征的Random Forest,其准确率得分为0.77,f1得分为0.80。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Identification of Lung Cancer in Smoker Person Using Ensemble Methods Based on Gene Expression Data
Cancer is a symptom of abnormal cell growth and is uncontrollable. Lung cancer is one of the most common types of cancer. Smoking is the leading cause of lung cancer. Early detection is essential because it can prevent lung cancer and get the proper treatment, such as a low-dose CT scan (LDCT). However, this effort still has drawbacks. With advances in DNA microarray technology, it is possible to measure the gene expression level of thousands of genes or cells in each tissue. The identification of lung cancer can be made using machine learning from the gene expression data (DNA microarray). In this study, a machine learning prediction model has been built using the Ensemble Methods, i.e. Random Forest and AdaBoost. The best model is Random Forest with 900 features and gets 0.77 for accuracy score and 0.80 for f1 score.
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