Multiclass Classification of Brain Cancer with Machine Learning Algorithms

Begüm Erkal, S. Başak, Alper Çiloğlu, Duygu Dede Sener
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引用次数: 5

Abstract

Brain cancer is one the most important disease to be treated all around the world. Classification of brain cancer using machine learning techniques has been widely studied by researchers. Microarray gene expression data are commonly used medical data to get observable results in this manner. In this study, multiclass classification of brain cancer is aimed by using different machine learning approaches. Some preprocessing methods were applied to get improved results. According to the result, feature selection has greatly affected the overall performance of each method in terms of overall accuracy and per class accuracy. Experimental results show that Multilayer Perceptron (MP) method has higher accuracy rate compared with other machine learning methods.
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基于机器学习算法的脑癌多类分类
脑癌是全世界最需要治疗的疾病之一。使用机器学习技术对脑癌进行分类已经被研究人员广泛研究。微阵列基因表达数据是常用的医学数据,以这种方式获得可观察的结果。在本研究中,通过使用不同的机器学习方法,对脑癌进行多类分类。采用了一些预处理方法,得到了较好的结果。结果表明,特征选择对各方法的总体准确率和每类准确率都有较大的影响。实验结果表明,与其他机器学习方法相比,多层感知器(Multilayer Perceptron, MP)方法具有更高的准确率。
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