使用机器学习方法对潜在的乳腺癌/结直肠癌病例进行分类

IF 0.4 Q4 ONCOLOGY
M. Jafarpour, A. Moeini, Niloofar Maryami, A. Nahvijou, Ayoub Mohammadian
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引用次数: 0

摘要

背景:通过基因表达对感染和健康个体的算法分类一直是包括癌症在内的许多领域研究人员感兴趣的话题。一些研究提出了许多解决方案,如神经网络和支持向量机(svm),以对各种癌症病例进行分类。这种分类提供了一定程度的准确性,这高度依赖于优化方法和合适的核。目的:本研究旨在提出一种有效利用机器学习方法对乳腺癌和结直肠癌(CRC)下的易发癌和健康病例进行分类的方法,提高分类过程的准确性。方法:本研究提出了一种诊断乳腺癌和结直肠癌易发个体的算法。该算法的新颖之处在于其合适的核和特征提取方法。本研究通过应用该算法,首先识别出与这些类型癌症密切相关的基因,然后尝试使用SVM寻找相关癌症的易感个体。目前的研究强调了与这些癌症相关的间接基因表达,这可能表明患者的健康状况并发症。为此,该算法由支持向量机与k-fold验证方法相结合组成。结果:与普通神经网络相比,结果证实了该方法的优越性能。该算法对乳腺癌和结直肠癌的识别准确率分别为98.077%和99.806%。还提供了因果关系的图形表示,以帮助研究人员更好地了解癌症或其他类型疾病的趋势。结论:特征提取方法对分类准确率影响很大。此外,依靠间接致病基因的表达,凸显了基因与疾病之间的因果关系。这种关系可以在临床领域形成马尔可夫模型,从而导致治疗路径和患者结果的预测。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Classification of Potential Breast/Colorectal Cancer Cases Using Machine Learning Methods
Background: The algorithmic classification of infected and healthy individuals by gene expression has been a topic of interest to researchers in numerous domains, including cancer. Several studies have presented numerous solutions, such as neural networks and support vector machines (SVMs), to classify a diverse range of cancer cases. Such classifications have provided some degrees of accuracy, which highly depend on optimization approaches and suitable kernels. Objectives: This study aimed at proposing a method to classify cancer-prone and healthy cases under breast cancer and colorectal cancer (CRC), using machine learning methods efficiently, increasing the accuracy of the classification process. Methods: This study presented an algorithm to diagnose individuals prone to breast cancer and CRC. The novelty of this algorithm lies in its suitable kernel and the feature extraction approach. By the application of this algorithm, this study first identified the genes closely associated with these types of cancers and, then, tried to find individuals susceptible to the concerned cancers using SVM. The present study highlighted the indirect gene expressions associated with these cancers, which might show health status complications for the patients. To this end, the algorithm consists of SVMs in conjunction with the k-fold method for validation. Results: The results confirmed the superior performance of this approach, compared to the common neural networks. The algorithm’s identification accuracy values were 98.077% and 99.806% for breast cancer and CRC, respectively. The graphic representation of the cause-effect relationships was also provided to help researchers better understand the trend of cancer or other types of diseases. Conclusions: The feature extraction method highly affects the accuracy of the classification. In addition, relying on indirect disease-triggering genes’ expressions highlights a cause-effect relationship between genes and diseases. Such relationships can form Markov models in the clinical domain leading to treatment paths and prediction of patient outcomes.
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来源期刊
CiteScore
1.10
自引率
0.00%
发文量
67
期刊介绍: International Journal of Cancer Management (IJCM) publishes peer-reviewed original studies and reviews on cancer etiology, epidemiology and risk factors, novel approach to cancer management including prevention, diagnosis, surgery, radiotherapy, medical oncology, and issues regarding cancer survivorship and palliative care. The scope spans the spectrum of cancer research from the laboratory to the clinic, with special emphasis on translational cancer research that bridge the laboratory and clinic. We also consider original case reports that expand clinical cancer knowledge and convey important best practice messages.
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