Detection of colon cancer based on microarray dataset using machine learning as a feature selection and classification techniques

被引:22
|
作者
Shafi, A. S. M. [1 ,2 ]
Molla, M. M. Imran [2 ]
Jui, Julakha Jahan [3 ]
Rahman, Mohammad Motiur [1 ]
机构
[1] Mawlana Bhashani Sci & Technol Univ, Dept Comp Sci & Engn, Tangail 1902, Bangladesh
[2] Khwaja Yunus Ali Univ, Fac Comp Sci & Engn, Sirajgonj 6751, Bangladesh
[3] Univ Malaysia Pahang, Fac Elect & Elect Engn, Pekan 26600, Pahang, Malaysia
来源
SN APPLIED SCIENCES | 2020年 / 2卷 / 07期
关键词
Colon cancer; Microarray data; Feature selection; Machine learning; Random forest; Cross validation; PARTICLE SWARM OPTIMIZATION; GENE; PREDICTION;
D O I
10.1007/s42452-020-3051-2
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
Microarray data is an increasingly important tool for providing information on gene expression for analysis and interpretation. Researchers attempt to utilize the smallest possible set of relevant gene expression profiles in most gene expression studies to enhance tumor identification accuracy. This research aims to analyze and predicts colon cancer data employing a machine learning approach and feature selection technique based on a random forest classifier. More particularly, our proposed method can reduce the burden of high dimensional data and allow faster calculations by combining the "Mean Decrease Accuracy" and "Mean Decrease Gini" as feature selection methods into a renowned classifier namely Random Forest, with the aim of increasing the prediction model's accuracy level. In addition, we have also shown a comparative model analysis with selection of features and model without selection of features. The extensive experimental results have demonstrated that the proposed model with feature selection is favorable and effective which triumphs the best performance of accuracy.
引用
收藏
页数:8
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