Conference Paper2020Cited by 15

Adaptive Feature Selection and Classification of Colon Cancer From Gene Expression Data: an Ensemble Learning Approach

Ashraful Islam, Mohammad Masudur Rahman, Eshtiak Ahmed, Faisal Arafat, Md Fazle Rabby

ACM International Conference on Computing Advancements (ICCA 2020)

ACM, pp. 1-7

  • Good Health and Well-being — Relevance: 91%
DOI: 10.1145/3377049.3377070URL

Abstract

Cancer research is one of the major and significant areas in medical research. A substantial number of research has been performed in this area and several methods have been employed. However, accuracy of cancer prediction is yet to reach near perfection as the conventional classification methods have several limitations. In recent times, microarray processed gene expression data has been used to predict cancer with significant accuracy. The gene expression data are usually high dimensional and comprises of relatively small number of samples which makes them difficult to classify. In order to achieve higher accuracy, ensembles method can be deployed which combines multiple classification methods. In this study, we have used the public colon cancer gene expression data set that consists of 62 instances having 2,000 attributes. An adaptive pre-processing procedure has been conducted including Linear Discriminant Analysis (LDA) and Principle Component Analysis (PCA) to cope up with the high dimensionality of the data. This was followed by building an ensemble learning model with k-Nearest Neighbors (kNN), Random Forest (RF), Kernel Support Vector Machines (KSVM), eXtreme Gradient Boosting (XGBoost), and Bayes Generalized Linear Model (GLM). Comparing with other classifiers, this study offers a significant improvement as our ensemble learning model gives higher accuracy than previously employed classification techniques. Thus the obtained accuracy is 91.67% with the scores 0.75, 1.00 and 0.85 of precision, recall and Matthews correlation coefficient (MCC) values respectively.

Keywords

  • Random forest
  • Principal component analysis
  • XGBoost
  • colon cancer
  • k-nearest neighbors
  • Linear discriminant analysis
  • Ensemble learning
  • gene expression data
  • kernel support vector machine
  • Dimensionality reduction

CCDS Authors

Ashraful Islam, PhD
Ashraful Islam

Ashraful Islam, PhD

  • Center Director, CCDS
  • Director, HCI Wing

Assistant Professor

Department of Computer Science and Engineering

Independent University, Bangladesh (IUB)

Published at

CHI, UIST, UbiComp, IJCNN, ICIP, HCII, ICDMW, BLP, IEEE Internet of Things Journal, JMIR, JMIR Human Factors, JMIR Formative Research, IEEE Access, Smart Agricultural Technology, Diabetes Research and Clinical Practice, PLOS ONE

Research Interests

Human-Computer Interaction, AI for Social Good, AI for Public Health, AI for Impact

Eshtiak Ahmed
Eshtiak Ahmed

Eshtiak Ahmed

  • Collaborator, HCI Wing

Doctoral Researcher

Gamification Group

Tampere University, Finland

Published at

UbiComp, ICIP, HCII, IEEE Access

Research Interests

Human-Computer Interaction, Human-Robot Interaction, Gamification and Assistive Technologies

References

  1. 1.F. Alexander Wolf, Philipp Angerer, Fabian J. Theis. (2018). SCANPY: large-scale single-cell gene expression data analysis. Genome biology, 19(1), 15[10.1186/s13059-017-1382-0]
  2. 2.Petra Perner. (2002). Data Mining - Concepts and Techniques.. Künstliche Intell., 16, 77
  3. 3.Uri Alon, Naama Barkai, Daniel A. Notterman, Kurt Gish, Suzanne E. Ybarra, David H. Mack, Arnold J. Levine. (1999). Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays. Proceedings of the National Academy of Sciences, 96(12), 6745–6750[10.1073/pnas.96.12.6745]
  4. 4.Κωνσταντίνα Κούρου, Themis P. Exarchos, Konstantinos Exarchos, Michalis V. Karamouzis, Dimitrios I. Fotiadis. (2014). Machine learning applications in cancer prognosis and prediction. Computational and Structural Biotechnology Journal, 13, 8–17[10.1016/j.csbj.2014.11.005]
  5. 5.Min Chen, Yixue Hao, Kai Hwang, Lu Wang, Lin Wang. (2017). Disease Prediction by Machine Learning Over Big Data From Healthcare Communities. IEEE Access, 5, 8869–8879[10.1109/access.2017.2694446]
  6. 6.Zena M. Hira, Duncan Gillies. (2015). A Review of Feature Selection and Feature Extraction Methods Applied on Microarray Data. Advances in Bioinformatics, 2015, 1–13[10.1155/2015/198363]
  7. 7.Leping Li, Clarice R. Weinberg, Thomas A. Darden, Lee G. Pedersen. (2001). Gene selection for sample classification based on gene expression data: study of sensitivity to choice of parameters of the GA/KNN method. Bioinformatics, 17(12), 1131–1142[10.1093/bioinformatics/17.12.1131]
  8. 8.Rick Kamps, Rita D. Brandão, B.J.C. van den Bosch, Aimée Paulussen, Sofia Xanthoulea, Marinus J. Blok, Andrea Romano. (2017). Next-Generation Sequencing in Oncology: Genetic Diagnosis, Risk Prediction and Cancer Classification. International Journal of Molecular Sciences, 18(2), 308[10.3390/ijms18020308]
  9. 9.Aik Choon Tan, David Gilbert. (2003). Ensemble machine learning on gene expression data for cancer classification.. PubMed, 2(3 Suppl), S75–83[link]
  10. 10.Kun Lan, Dantong Wang, Simon Fong, Liansheng Liu, Kelvin K. L. Wong, Nilanjan Dey. (2018). A Survey of Data Mining and Deep Learning in Bioinformatics. Journal of Medical Systems, 42(8), 139[10.1007/s10916-018-1003-9]
  11. 11.Chen‐Hsiang Yeang, Sridhar Ramaswamy, Pablo Tamayo, Sayan Mukherjee, Ryan Rifkin, Michael Angelo, Michael Reich, Eric S. Lander, Jill P. Mesirov, Todd Golub. (2001). Molecular classification of multiple tumor types. Bioinformatics, 17(suppl_1), S316–S322[10.1093/bioinformatics/17.suppl_1.s316]
  12. 12.Hala Alshamlan, Ghada Badr, Yousef Al-Ohali. (2015). mRMR-ABC: A Hybrid Gene Selection Algorithm for Cancer Classification Using Microarray Gene Expression Profiling. BioMed Research International, 2015, 1–15[10.1155/2015/604910]
  13. 13.(1971). International Journal of Cancer. JNCI Journal of the National Cancer Institute[10.1093/jnci/47.1.267]
  14. 14.John de Caestecker. (2007). Clinical gastroenterology and hepatology. Gut, 56(10), 1486–1487[10.1136/gut.2007.121491]
  15. 15.Steffen Backert, Marcos Gelos, Ursula Kobalz, Marie‐Luise Hanski, Christian B�hm, Benno Mann, Nicole L�vin, Alexei Gratchev, Ulrich Mansmann, Mary Pat Moyer, Ernst-Otto Riecken, C. Hanski. (1999). Differential gene expression in colon carcinoma cells and tissues detected with a cDNA array. International Journal of Cancer, 82(6), 868–874[10.1002/(sici)1097-0215(19990909)82:6<868::aid-ijc16>3.0.co;2-w]
  16. 16.Elizabeth Alwers, Min Jia, Matthias Kloor, Hendrik Bläker, Hermann Brenner, Michael Hoffmeister. (2018). Associations Between Molecular Classifications of Colorectal Cancer and Patient Survival: A Systematic Review. Clinical Gastroenterology and Hepatology, 17(3), 402–410.e2[10.1016/j.cgh.2017.12.038]
  17. 17.Muchenxuan Tong, Kunhong Liu, Chungui Xu, Wen‐Bin Ju. (2013). An ensemble of SVM classifiers based on gene pairs. Computers in Biology and Medicine, 43(6), 729–737[10.1016/j.compbiomed.2013.03.010]
  18. 18.Huaming Chen, Hong Zhao, Jun Shen, Rui Zhou, Qingguo Zhou. (2015). Supervised Machine Learning Model for High Dimensional Gene Data in Colon Cancer Detection. , 134–141[10.1109/bigdatacongress.2015.28]
  19. 19.Saima Rathore, Mutawarra Hussain, Asifullah Khan. (2014). GECC: Gene Expression Based Ensemble Classification of Colon Samples. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 11(6), 1131–1145[10.1109/tcbb.2014.2344655]
  20. 20.Ashwinikumar Kulkarni, B. Sathish Kumar, Vadlamani Ravi, Upadhyayula Suryanarayana Murthy. (2010). Colon cancer prediction with genetics profiles using evolutionary techniques. Expert Systems with Applications, 38(3), 2752–2757[10.1016/j.eswa.2010.08.065]
  21. 21.Javier Herrero, Ramón Díaz‐Uriarte, Joaquı́n Dopazo. (2003). Gene expression data preprocessing. Bioinformatics, 19(5), 655–656[10.1093/bioinformatics/btg040]
  22. 22.Danh V. Nguyen, Naisyin Wang, Raymond J. Carroll. (2021). Evaluation of Missing Value Estimation for Microarray Data. Journal of Data Science, 2(4), 347–370[10.6339/jds.2004.02(4).170]
  23. 23.Xuan Long, Zhigang Deng, Guoqiang Li, Ziwei Wang. (2018). Identification of critical genes to predict recurrence and death in colon cancer: integrating gene expression and bioinformatics analysis. Cancer Cell International, 18(1), 139[10.1186/s12935-018-0640-x]
  24. 24.Ho-Sun Shon, Gyoyong Sohn, Kwang Su Jung, S. Y. Kim, Eun Jong, Keun Ho Ryu. (2009). Gene Expression Data Classification using Discrete Wavelet Transform.. BIOCOMP, 204–208
  25. 25.Zhen-Yu Chen, Jianping Li. (2007). A Multiple Kernel Support Vector Machine Scheme for Simultaneous Feature Selection and Rule-Based Classification. Lecture notes in computer science, 441–448[10.1007/978-3-540-71701-0_44]
  26. 26.Yang Liu, Jin Zhou, Yuehui Chen. (2008). Ensemble Classification for Cancer Data. , 269–273[10.1109/bmei.2008.161]
  27. 27.Anil K. Rustgi. (2007). The genetics of hereditary colon cancer. Genes & Development, 21(20), 2525–2538[10.1101/gad.1593107]

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  2. 2.Sarah Osama, Hassan Shaban, Abdelmgeid A. Ali. (2022). Gene reduction and machine learning algorithms for cancer classification based on microarray gene expression data: A comprehensive review. Expert Systems with Applications, 213, 118946[10.1016/j.eswa.2022.118946]
  3. 3.Nur Ghaniaviyanto Ramadhan, Adiwijaya, Warih Maharani, Alfian Akbar Gozali. (2024). Chronic Diseases Prediction Using Machine Learning With Data Preprocessing Handling: A Critical Review. IEEE Access, 12, 80698–80730[10.1109/access.2024.3406748]
  4. 4.Faisal Arafat, Thaharim Khan, Atanu Das Bapon, Md. Ibrahim Khan, Sheak Rashed Haider Noori. (2021). A Deep Learning Approach to Predict Chronic Kidney Disease in Human. 2021 IEEE 12th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON), 1010–1015[10.1109/iemcon53756.2021.9623101]
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  7. 7.M. Kalaivani, K. Abirami, K. Dharmarajan. (2023). An Early Stage Determination of Colon Cancer Through Deep Neural Network. , 1–6[10.1109/icaiihi57871.2023.10489621]
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  11. 11.Jie Liu, Zhong Cheng, Jiamin Zhang, Kejun Liu, Mengjie Liu. (2023). Diagnosis of gastric cancer based on hybrid genes selection approach. Biotechnology and Genetic Engineering Reviews, 40(3), 2776–2795[10.1080/02648725.2023.2202524]
  12. 12.Namitha T M, Vinod Kumar R S. (2025). Automated Approaches For Detecting And Classifying Colon Cancer: A Comprehensive Study. , 1–7[10.1109/ictest64710.2025.11042462]
  13. 13.Sourabh Kumar Singh, Shailendra Kumar Mahobe, Ishika Shailendra Pandey, Gauri Shivaji Patole, Priti Vitthal Jadhav. (2025). A Comprehensive Study of CNN Models for Colon Cancer Detection. , 1–8[10.1109/ciacon65473.2025.11189452]
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