Skip to main content
SpringerLink
Log in

Improved Multi-label Medical Text Classification Using Features Cooperation

  • Conference paper
  • First Online:
Innovative Systems for Intelligent Health Informatics (IRICT 2020)

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 72))

  • 1128 Accesses

Abstract

Medical text categorization is a valuable area of text classification due to the massive growth in the amount of medical data, most of which is unstructured. Reading and understanding the information contained in millions of medical documents is a time-consuming process. Automatic text classification aims to automatically classify text documents into one or more predefined categories according to several criteria such as the type of output (multi-label or mono label). Feature extraction task plays an important role in text classification. Extracting informative features highly increases the performance of the classification models and reduces the computational complexity. Traditional feature extraction methods are based on handcrafted features which mainly depend on prior knowledge. The use of these features may involve an insignificant representation. Doc2vec is a way to generate a vector of informative and essential features that are specific to a document. In this paper, the impact of combining handcrafted and doc2vec features in the multi-label document classification scenario is analyzed by proposing a system named MUL-MEDTEC. The one-versus-all classification strategy based on logistic regression is adopted in this study to predict for each medical text it to one or several labels. Experimental results based on Ohsumed medical dataset are very encouraging with based classification accuracy equal to 0.92 as global precision.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Hughes, M., Li, I., Kotoulas, S., Suzumura, T.: Medical text classification using convolutional neural networks. Stud. Health Technol. Inf. 235, 246–250 (2017)

    Google Scholar 

  2. Lenivtceva, J., Slasten, E., Kashina, M., Kopanitsa, G..: Applicability of machine learning methods to multi-label medical text classification. In: Krzhizhanovskaya, V., et al. (eds.) Computational Science – ICCS, Springer, Cham, pp. 509–522 (2020)

    Google Scholar 

  3. Benzebouchi, N.E., Azizi, N., Hammami, N.E., Schwab, D., Khelaifia, M.C.E., Aldwairi, M.: Authors' writing styles based authorship identification system using the text representation vector. In: 16th International Multi-Conference on Systems, Signals & Devices (SSD), IEEE, Istanbul, Turkey, pp. 371–376, 21–24 March (2019)

    Google Scholar 

  4. Benzebouchi, N.E., Azizi, N., Aldwairi, M., Farah, N.: Multi-classifier system for authorship verification task using word embeddings. In: 2018 2nd International Conference on Natural Language and Speech Processing (ICNLSP), IEEE, Algiers, Algeria, pp. 1–6, 25–26 April (2018)

    Google Scholar 

  5. Qing, L., Linhong, W., Xuehai, D.: A novel neural network-based method for medical text classification. Future Int. 11, 255–268 (2019).

    Google Scholar 

  6. Alkhatib, W., Rensing, C., Silberbauer, J.: Multi-label text classification using semantic features and dimensionality reduction with autoencoders. In: Gracia, J., Bond, F., McCrae, J., Buitelaar, P., Chiarcos, C., Hellmann, S. (eds.) Language, Data, and Knowledge. LDK 2017, Lecture Notes in Computer Science, vol. 10318. Springer, Cham, pp. 380–394 (2017)

    Google Scholar 

  7. Lenc, L., Kral, P.: Word Embeddings for multi-label document classification. In: Proceedings of Recent Advances in Natural Language Processing, Varna, Bulgaria, pp. 431–437, 4–6 September (2017)

    Google Scholar 

  8. Guo, Y., Chung, F., Li, G.: An ensemble embedded feature selection method for multi-label clinical text classification. In: IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 823–826 (2016)

    Google Scholar 

  9. Azizi, N., Farah, N., Sellami, M.: Ensemble classifier construction for Arabic handwritten recongnition. In: 7th International Workshop on Systems, Signal Processing and their Applications, WoSSPA, pp. 271–274 (2011)

    Google Scholar 

  10. Lee, H., Yoon, Y.: Engineering doc2vec for automatic classification of product descriptions on O2O applications. Electron. Commer. Res. 18(3), 433–456 (2017)

    Google Scholar 

  11. Kim, D., Seo, D., Cho, S., Kang, P.: Multi-co-training for document classification using various document representations: TF–IDF, LDA, and Doc2Vec. Inf. Sci. 477, 15–29 (2019)

    Google Scholar 

  12. Wan, S., Mak, M.-W., Kung, S.-Y.: mPLR-Loc: An adaptive decision multi-label classifier based on penalized logistic regression for protein subcellular localization prediction. Anal. Biochem. 473, 14–27 (2015)

    Article  Google Scholar 

  13. Chalkidis, I., Fergadiotis, M., Malakasiotis, P., Androutsopoulos, I.: Large-scale multi-label text classification on eu legislation, arXiv preprint arXiv:1906.02192 (2019)

  14. Hoque, M.T., Islam, A., Ahmed, E., Mamun, K.A., Huda, M.N.: Analyzing performance of different machine learning approaches with Doc2vec for classifying sentiment of Bengali natural language. In: 2019 International Conference on Electrical, Computer and Communication Engineering (ECCE). IEEE (2019)

    Google Scholar 

  15. Dzisevic, R., Sesok, D.: Text classification using different feature extraction approaches. In: 2019 Open Conference of Electrical, Electronic and Information Sciences (eStream), Vilnius, Lituanie, pp. 1–4 (2019)

    Google Scholar 

  16. Resham, N.W., Anuradha, D.: Thakare2.: a review of feature extraction methods for text, International Journal of Advance Engineering and Research Development (2018)

    Google Scholar 

  17. Le, Q., Mikolov, T.: Distributed representations of sentences and documents. In: Paper presented at the proceedings of the 31st international conference on international conference on machine learning (2014)

    Google Scholar 

  18. Mikolov, T., Sutskever, I., Chen, K., Corrado, G., Dean, J.: Distributed representations of words and phrases and their compositionality. In: Paper Presented at the Proceedings of the 26th International Conference on Neural Information Processing Systems (2013)

    Google Scholar 

  19. https://shuzhanfan.github.io/2018/08/understanding-word2vec-and-doc2vec/ Accessed 15 Aug 2020

  20. You, X., Zhang, Y., Li, B., Lv, X., Han, J.: VDIF-M: Multi-label classification of vehicle defect information collection based on Seq2seq model. In: Yin, Y., Li, Y., Gao, H., Zhang, J. (eds.) Mobile Computing, Applications, and Services. MobiCASE 2019, Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol. 290. Springer, Cham (2019)

    Google Scholar 

  21. Pant, P., Sabitha, A.S., Choudhury, T., Dhingra, P.: Multi-label classification trending challenges and approaches. In: Rathore, V., Worring, M., Mishra, D., Joshi, A., Maheshwari, S., (eds.) Emerging Trends in Expert Applications and Security. Advances in Intelligent Systems and Computing, vol. 841, Springer, Singapore, pp. 433–444 (2019)

    Google Scholar 

  22. Ganda, D., Buch, R.: A survey on multi label classification. Recent Trends Program. Lang. 5(1), 19–23 (2018)

    Google Scholar 

  23. https://disi.unitn.it/moschitti/corpora.htm Accessed 24 July 2020

  24. Al-Salemi, B., Mohd Noah, S.A., Ab Aziz, M.J.: RFBoost: an improved multi-label boosting algorithm and its application to text categorization. Knowl.-Based Syst. 103, 104–117 (2016)

    Article  Google Scholar 

  25. Al-Salemi, B., Masri, A., Noah, S.A.M: Feature ranking for enhancing boosting-based multi-label text categorization. Expert Syst. Appl. 113, 531−543 (2018)

    Google Scholar 

  26. Parlak, B., Alper, K.U.: The impact of feature selection on medical document classification. In: 2016 11th Iberian Conference on Information Systems and Technologies (CISTI). IEEE (2016)

    Google Scholar 

  27. Burkhardt, S., Stefan, K.: Online multi-label dependency topic models for text classification. Mach. Learn. 107(5), 859–886 (2018)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Labged Laboratory of Electronic Document Management, Badji Mokhtar University, Annaba, Algeria

    Rim Chaib, Nabiha Azizi & Nawel Zemmal

  2. Computer Science Department, Badji Mokhtar University, 23000, Annaba, Algeria

    Rim Chaib & Nabiha Azizi

  3. Department of Mathematics and Computer Science, Mohamed Cherif Messaadia University, 41000, Souk-Ahras, Algeria

    Nawel Zemmal

  4. LIG-GETALP Laboratory, Grenoble Alpes University, Grenoble, France

    Didier Schwab

  5. College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar

    Samir Brahim Belhaouari

Authors
  1. Rim Chaib
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Nabiha Azizi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Nawel Zemmal
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Didier Schwab
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Samir Brahim Belhaouari
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. College of Computer Science and Engineering, Taibah University, Medina, Saudi Arabia

    Faisal Saeed

  2. School of Computing, Information Systems Department, Universiti Utara Malaysia, Sintok, Malaysia

    Fathey Mohammed

  3. Sanaa’a Community College, Sana'a, Yemen

    Abdulaziz Al-Nahari

Rights and permissions

Reprints and permissions

Copyright information

© 2021 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Chaib, R., Azizi, N., Zemmal, N., Schwab, D., Belhaouari, S.B. (2021). Improved Multi-label Medical Text Classification Using Features Cooperation. In: Saeed, F., Mohammed, F., Al-Nahari, A. (eds) Innovative Systems for Intelligent Health Informatics. IRICT 2020. Lecture Notes on Data Engineering and Communications Technologies, vol 72. Springer, Cham. https://doi.org/10.1007/978-3-030-70713-2_7

Download citation

Publish with us

Policies and ethics

Search

Navigation