Effects of hybrid non-linear feature extraction method on different data sampling techniques for liver disease prediction

Yasmin, R.; Amin, R.; Reza, M.

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Journal of Future Sustainability

ISSN 2816-8151 (Online) - ISSN 2816-8143 (Print) Quarterly Publication Volume 2 Issue 2 pp. 57-64 , 2022

Effects of hybrid non-linear feature extraction method on different data sampling techniques for liver disease prediction Pages 57-64 Download PDF

Authors: Rubia Yasmin, Ruhul Amin, Md. Shamim Reza

DOI: 10.5267/j.jfs.2022.9.005

Keywords: Liver Disease, Imbalanced Data, Non-linear Feature Extraction, Prediction

Abstract: Liver disease indicates inflammatory condition of the liver, liver cirrhosis, cancer, or an overload of toxic substances. A liver transplant may reinstate and extend life if a patient has severe liver disease. In the last few years, machine learning (ML) based diagnosis systems have played a vital role in assessing liver patients which eventually leads to proper treatment and saves human life. In this study, we try to predict liver patients by adopting a hybrid feature extraction method to enhance the performance of the ML algorithm. Medical data frequently exhibits non-linear patterns and class imbalances. This is undesirable for the majority of ML algorithms and degrades performance. Here, we present a hybrid feature space that combines t-SNE, Isomap nonlinear features, and kernel principal components that can explain 90% of the variation in the data as a solution to this issue. Before feeding the ML model, data preprocessing techniques including class balancing, identifying outliers, and impute missing values are used. A simulation study and ensemble learning also conducted to justify the proposed prediction performances. Our suggested hybrid non-linear feature exhibits a 2-20 % improvement over existing studies and the ensemble classifier achieved an ideal and outstanding accuracy of 91.33 %.

How to cite this paper

 Yasmin, R., Amin, R & Reza, M. (2022). Effects of hybrid non-linear feature extraction method on different data sampling techniques for liver disease prediction.Journal of Future Sustainability, 2(2), 57-64.

Refrences

Bahramirad, S., Mustapha, A., & Eshraghi, M. (2013, September). Classification of liver disease diagnosis: a comparative study. In 2013 Second International Conference on Informatics & Applications (ICIA) (pp. 42-46). IEEE.
Batista, G. E. A. P. A., Prati, R. C., & Monard, M. C. (2004). A study of the behavior of several methods for balancing machine learning training data. ACM SIGKDD Explorations Newsletter, 6(1), 20–29. https://doi.org/10.1145/1007730.1007735
Benjamin Wedro, MD, FACEP, F. (n.d.). Liver Disease: Early Signs, Symptoms, Treatment, Stages, Types & Diet. Retrieved April 26, 2022, from https://www.medicinenet.com/liver_disease/article.htm
CESARSOUZA. (n.d.). Kernel Functions for Machine Learning Applications – César Souza. Retrieved May 4, 2022, from http://crsouza.com/2010/03/17/kernel-functions-for-machine-learning-applications/
Chkirbene, Z., Erbad, A., Hamila, R., Gouissem, A., Mohamed, A., Guizani, M., & Hamdi, M. (2021). A Weighted Machine Learning-Based Attacks Classification to Alleviating Class Imbalance. IEEE Systems Journal, 15(4), 4780–4791. https://doi.org/10.1109/JSYST.2020.3033423
Ezukwoke, K., Zareian, S., & Regression, L. (2019). KERNEL METHODS FOR PRINCIPAL COMPONENT ANALYSIS ( PCA ) A comparative study of classical and kernel pca . December. https://doi.org/10.13140/RG.2.2.17763.09760
Fathi, M., Nemati, M., Mohammadi, S. M., & Reza, A.-K. (2020). A MACHINE LEARNING APPROACH BASED ON SVM FOR CLASSIFICATION OF LIVER DISEASES. 32(2), 1–9. https://doi.org/10.4015/S1016237220500180
Gulia, A., Vohra, R., & Rani, P. (2014). Liver Patient Classification Using Intelligent Techniques. 5(4), 5110–5115.
Index of /ml/machine-learning-databases/00225. (n.d.). Retrieved May 1, 2022, from https://archive.ics.uci.edu/ml/machine-learning-databases/00225/
Junsomboon, N., & Phienthrakul, T. (2017). Combining over-sampling and under-sampling techniques for imbalance dataset. ACM International Conference Proceeding Series, Part F1283(1), 243–247. https://doi.org/10.1145/3055635.3056643
Kang, M., & Tian, J. (2018). Machine Learning : Data Pre-processing. 111–130.
Kumar, P., & Thakur, R. S. (2019). Early Detection of the Liver Disorder from Imbalance Liver Function Test Datasets. 4, 179–186.
Kumar, P., & Thakur, R. S. (2021). Liver disorder detection using variable- neighbor weighted fuzzy K nearest neighbor approach. Multimedia Tools and Applications, 80(11), 16515–16535. https://doi.org/10.1007/s11042-019-07978-3
Leon-Medina, J. X., Anaya, M., Pozo, F., & Tibaduiza, D. (n.d.). Nonlinear Feature Extraction Through Manifold Learning in an Electronic Tongue Classification Task. https://doi.org/10.3390/s20174834
Rodrigues, G. (n.d.). Automatic feature extraction with t-SNE | by Gonçalo Rodrigues | Jungle Book | Medium. Retrieved May 4, 2022, from https://medium.com/jungle-book/automatic-feature-extraction-with-t-sne-62826ce09268
Singh, J., Bagga, S., & Kaur, R. (2020). Software-based Prediction of Liver Disease with Feature Selection Software-based Prediction of Liver Disease with Feature Selection and Classification Techniques and Classification Techniques. Procedia Computer Science, 167(2019), 1970–1980. https://doi.org/10.1016/j.procs.2020.03.226
Stoppler, M. C., & MD. (n.d.). Liver Disease Symptoms, Signs & Causes. Retrieved April 23, 2022, from https://www.medicinenet.com/liver_disease_symptoms_and_signs/symptoms.htm
WHO | World Health Organization. (n.d.). Retrieved April 26, 2022, from https://www.who.int/
Yousaf, M., Rehman, T. U., & Jing, L. (2020). An Extended Isomap Approach for Nonlinear Dimension Reduction. SN Computer Science, 1(3). https://doi.org/10.1007/s42979-020-00179-y