Mostafa Atlam, Hanaa Torkey, Hanaa Salem, N. El-Fishawy
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The enrolled classification techniques in CTFS function for selection are K-Nearest Neighbors (K-NN) and Extreme Gradient Boosting (XGBoosting) optimized by Bayesian Parameter Tuning (BPT). The feature selection methods used to investigate the performance of CTFS function are Univariate Feature Selection (UFS) and Feature Importance (FI). The classification stage is carried out after the feature selection stage using three machine learning (ML) algorithms, Naïve Bayes (NB), Linear Support Vector Machine (LSVM), and Random Forest (RF). Results shows that, using XGBoosting optimized by BPT for FS outperforms FI method in terms of increasing the prediction accuracies along with minimum number of features but with higher running time. 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引用次数: 7
摘要
医学数据的准确分类是完善诊断预测系统和确定治疗目标的关键。从下一代测序技术输出的大量基因中提取疾病相关基因是分析基因表达数据的一大挑战。因此,消除不相关和冗余的基因是处理数据进行预测的重要步骤。我们的目标是通过基因表达更准确地预测样本细胞中癌症疾病的存在。本文提出了一种新的特征选择方法,即CTFS (Classification Technique as Feature Selection),从分类的大量基因表达中提取出一个子集(少量)的基因,以提高癌症预测结果。CTFS函数中用于选择的分类技术包括k -最近邻(K-NN)和贝叶斯参数调优(BPT)优化的极限梯度增强(XGBoosting)。用于研究CTFS函数性能的特征选择方法是单变量特征选择(UFS)和特征重要性(FI)。分类阶段在特征选择阶段之后进行,使用三种机器学习(ML)算法,Naïve贝叶斯(NB),线性支持向量机(LSVM)和随机森林(RF)。结果表明,使用BPT优化的XGBoosting方法在提高预测精度和特征数量最少的情况下优于FI方法,但运行时间更长。在精度方面,K-NN在FS中的性能优于所有其他FS方法,当在模拟数据集上使用LSVM时,提供高达100%的精度。
A New Feature Selection Method for Enhancing Cancer Diagnosis Based on DNA Microarray
Accurately classifying medical data is critical for improving diagnostic prediction system and identifying threptic targets for treatments. Analysing gene expression data has a major challenge in extracting disease-related genes from the large number of genes output from next generation sequencing technology. Therefore, eliminating irrelevant and redundant genes is a major step to process data for prediction. Our objective is to predict more accurately the presence of cancer disease in a sample cell from the gene expression.In this paper, we create a function called Classification Technique as Feature Selection (CTFS) as a new feature selection (FS) method to extract a subset (small number) of genes from classified big number of genes expression to improve cancer prediction result. The enrolled classification techniques in CTFS function for selection are K-Nearest Neighbors (K-NN) and Extreme Gradient Boosting (XGBoosting) optimized by Bayesian Parameter Tuning (BPT). The feature selection methods used to investigate the performance of CTFS function are Univariate Feature Selection (UFS) and Feature Importance (FI). The classification stage is carried out after the feature selection stage using three machine learning (ML) algorithms, Naïve Bayes (NB), Linear Support Vector Machine (LSVM), and Random Forest (RF). Results shows that, using XGBoosting optimized by BPT for FS outperforms FI method in terms of increasing the prediction accuracies along with minimum number of features but with higher running time. The performance of K-NN in FS outperforms all other FS methods in terms of accuracies providing an accuracy that is up to 100% when applied with LSVM on simulation dataset.