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Deep Ensemble Stacked Technique for the Classification of Liver Disease using Artificial Neural Networks at the Base Level and Random Forest at the Meta Level

by Rohini A. Bhusnurmath, Shivaleela Betageri
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 186 - Number 22
Year of Publication: 2024
Authors: Rohini A. Bhusnurmath, Shivaleela Betageri
10.5120/ijca2024923664

Rohini A. Bhusnurmath, Shivaleela Betageri . Deep Ensemble Stacked Technique for the Classification of Liver Disease using Artificial Neural Networks at the Base Level and Random Forest at the Meta Level. International Journal of Computer Applications. 186, 22 ( May 2024), 42-48. DOI=10.5120/ijca2024923664

@article{ 10.5120/ijca2024923664,
author = { Rohini A. Bhusnurmath, Shivaleela Betageri },
title = { Deep Ensemble Stacked Technique for the Classification of Liver Disease using Artificial Neural Networks at the Base Level and Random Forest at the Meta Level },
journal = { International Journal of Computer Applications },
issue_date = { May 2024 },
volume = { 186 },
number = { 22 },
month = { May },
year = { 2024 },
issn = { 0975-8887 },
pages = { 42-48 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume186/number22/deep-ensemble-stacked-technique-for-the-classification-of-liver-disease-using-artificial-neural-networks-at-the-base-level-and-random-forest-at-the-meta-level/ },
doi = { 10.5120/ijca2024923664 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-05-31T22:31:56.852905+05:30
%A Rohini A. Bhusnurmath
%A Shivaleela Betageri
%T Deep Ensemble Stacked Technique for the Classification of Liver Disease using Artificial Neural Networks at the Base Level and Random Forest at the Meta Level
%J International Journal of Computer Applications
%@ 0975-8887
%V 186
%N 22
%P 42-48
%D 2024
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Liver is the most noticeable and metabolically active organs in the human body. Damage to this organ might result in liver failure and a loss of life. The early detection of liver infection is critical for effective therapy. The main goal of the research is to identify liver illness using machine learning and deep learning classification algorithms. The proposed "Deep ensemble stacked model" is implemented on the “Indian liver Patient Dataset”. An attempt is made to analyze the data through various pre-processing and visualization techniques to understand which factors affect the liver. An artificial neural network is used as the model's primary training and testing tool. The new training set is created for the meta-level consisting of base-level predictions. The random forest classifier is used at the meta level which serves as a meta classifier for the final prediction. The deep ensemble stacked model performed better than the most recent research with 98.29% of classification accuracy, 97.43% of precision, 100% of recall rate, and 98.69% of f1-score. To evaluate the effectiveness of the suggested approach, the outcomes are also contrasted with well-known machine and deep learning models.

References
  1. Siri, Sangeeta K., S. Pramod Kumar, and Mrityunjaya V. Latte. "Threshold-based new segmentation model to separate the liver from CT scan images." IETE Journal of Research 68, no. 6 (2022): 4468-4475. https://doi.org/10.1080/03772063.2020.1795938
  2. Chlebus, Grzegorz, Andrea Schenk, Jan Hendrik Moltz, Bram van Ginneken, Horst Karl Hahn, and Hans Meine. "Automatic liver tumor segmentation in CT with fully convolutional neural networks and object-based postprocessing." Scientific reports 8, no. 1 (2018): 1-7. https://doi.org/10.1038/s41598-018-33860-7
  3. Singh, Jagdeep, Sachin Bagga, and Ranjodh Kaur. "Software-based prediction of liver disease with feature selection and classification techniques." Procedia Computer Science 167 (2020): 1970-1980. https://doi.org/10.1016/j.procs.2020.03.226
  4. Rahman, AKM Sazzadur, FM Javed Mehedi Shamrat, Zarrin Tasnim, Joy Roy, and Syed Akhter Hossain. "A comparative study on liver disease prediction using supervised machine learning algorithms." International Journal of Scientific & Technology Research 8, no. 11 (2019): 419-422.
  5. Rabbi, Md Fazle, SM Mahedy Hasan, Arifa Islam Champa, Md AsifZaman, and Md Kamrul Hasan. "Prediction of liver disorders using machine learning algorithms: a comparative study." In 2020 2nd International Conference on Advanced Information and Communication Technology (ICAICT), pp. 111-116. IEEE, 2020. doi: 10.1109/ICAICT51780.2020.9333528.
  6. Ghosh, Mounita, Md Raihan, Mohsin Sarker, M. Raihan, Laboni Akter, Anupam Kumar Bairagi, Sultan S. Alshamrani, and Mehedi Masud. "A Comparative Analysis of Machine Learning Algorithms to Predict Liver Disease." Intelligent Automation & Soft Computing 30, no. 3 (2021).
  7. Musleh, Musleh M., Eman Alajrami, Ahmed J. Khalil, Bassem S. Abu-Nasser, Alaa M. Barhoom, and SS Abu Naser. "Predicting liver patients using artificial neural network." International Journal of Academic Information Systems Research (IJAISR) 3, no. 10 (2019).
  8. Jeyalakshmi, K., and R. Rangaraj. "Accurate liver disease prediction system using convolutional neural network." Indian Journal of Science and Technology 14, no. 17 (2021): 1406-1421. https ://doi.org/10.17485/IJST/v14i17.451
  9. Kuzhippallil, Maria Alex, Carolyn Joseph, and A. Kannan. "Comparative analysis of machine learning techniques for indian liver disease patients." In 2020 6th International Conference on Advanced Computing and Communication Systems (ICACCS), pp. 778-782. IEEE, 2020. DOI:10.1109/ICACCS48705.2020.9074368
  10. Singh, Jagdeep, Sachin Bagga, and Ranjodh Kaur. "Software-based prediction of liver disease with feature selection and classification techniques." Procedia Computer Science 167 (2020): 1970-1980. https://doi.org/10.1016/j.procs.2020.03.226
  11. Dua, D. and Graff, C. (2019). UCI Machine Learning Repository [http://archive.ics.uci.edu/ml]. Irvine, CA: University of California, School of Information and Computer Science.
  12. Gogi, Vyshali J., and M. N. Vijayalakshmi. "Prognosis of liver disease: Using Machine Learning algorithms." In 2018 International Conference on Recent Innovations in Electrical, Electronics & Communication Engineering (ICRIEECE), pp. 875-879. IEEE, 2018. DOI: 10.1109/ICRIEECE44171.2018.9008482
  13. Ramana, Bendi Venkata, M. Surendra Prasad Babu, and N. B. Venkateswarlu. "A critical study of selected classification algorithms for liver disease diagnosis." International Journal of Database Management Systems 3, no. 2 (2011): 101-114.
  14. Aljahdali, Sultan, and Syed Naimatullah Hussain. "Comparative prediction performance with support vector machine and random forest classification techniques." International journal of computer applications 69, no. 11 (2013). DOI: 10.5120/11885-7922
  15. Mostafa, Fahad, Easin Hasan, Morgan Williamson, and Hafiz Khan. "Statistical machine learning approaches to liver disease prediction." Livers 1, no. 4 (2021): 294-312. https://doi.org/10.3390/livers1040023
  16. Nahar, Nazmun, Ferdous Ara, Md Arif Istiek Neloy, Vicky Barua, Mohammad Shahadat Hossain, and Karl Andersson. "A comparative analysis of the ensemble method for liver disease prediction." In 2019 2nd International Conference on Innovation in Engineering and Technology (ICIET), pp. 1-6. IEEE, 2019. DOI:10.1109/ICIET48527.2019.9290507
  17. Nahar, Nazmun, and Ferdous Ara. "Liver disease prediction by using different decision tree techniques." International Journal of Data Mining & Knowledge Management Process 8, no. 2 (2018): 01-09. DOI:10.5121/ijdkp.2018.8201
  18. Yao, Zhenjie, Jiangong Li, Zhaoyu Guan, Yancheng Ye, and Yixin Chen. "Liver disease screening based on densely connected deep neural networks." Neural Networks 123 (2020): 299-304.
  19. Ali, Maria, Muhammad Nasim Haider, Saima Anwar Lashari, Wareesa Sharif, Abdullah Khan, and Dzati Athiar Ramli. "Stacking Classifier with Random Forest functioning as a Meta Classifier for Diabetes Diseases Classification." Procedia Computer Science 207 (2022): 3459-3468. https://doi.org/10.1016/j.procs.2022.09.404
  20. Özyurt, Fatih, Türker Tuncer, Engin Avci, Mustafa Koç, and İhsan Serhatlioğlu. "A novel liver image classification method using perceptual hash-based convolutional neural network." Arabian Journal for Science and Engineering 44, no. 4 (2019): 3173-3182. https://doi.org/10.1007/s13369-018-3454-1
  21. Bhardwaj, Arpit, and Aruna Tiwari. "Breast cancer diagnosis using genetically optimized neural network model." Expert Systems with Applications 42, no. 10 (2015): 4611-4620. https://doi.org/10.1016/j.eswa.2015.01.065
  22. Papadopoulos, Marios C., Paulo M. Abel, Dan Agranoff, August Stich, Edward Tarelli, B. Anthony Bell, Timothy Planche et al. "A novel and accurate diagnostic test for human African trypanosomiasis." The Lancet 363, no. 9418 (2004): 1358-1363. https://doi.org/10.1016/S0140-6736(04)16046-7
  23. Wu, Chieh-Chen, Wen-Chun Yeh, Wen-Ding Hsu, Md Mohaimenul Islam, Phung Anh Alex Nguyen, Tahmina Nasrin Poly, Yao-Chin Wang, Hsuan-Chia Yang, and Yu-Chuan Jack Li. "Prediction of fatty liver disease using machine learning algorithms." Computer methods and programs in biomedicine 170 (2019): 23-29. https://doi.org/10.1016/j.cmpb.2018.12.032
  24. Aljahdali, Sultan, and Syed Naimatullah Hussain. "Comparative prediction performance with support vector machine and random forest classification techniques." International journal of computer applications 69, no. 11 (2013). DOI:10.5120/11885-7922
  25. Al Telaq, Burair Hassan, and Nabil Hewahi. "Prediction of Liver Disease using Machine Learning Models with PCA." In 2021 International Conference on Data Analytics for Business and Industry (ICDABI), pp. 250-254. IEEE, 2021. doi: 10.1109/ICDABI53623.2021.9655897.
  26. Veena, G. S., D. Sneha, Deepti Basavaraju, and Tripti Tanvi. "Effective analysis and diagnosis of liver disorder." In 2018 International Conference on Communication and Signal Processing (ICCSP), pp. 0086-0090. IEEE, 2018. doi: 10.1109/ICCSP.2018.8524347.
  27. Hossen, Md Sagar, Imdadul Haque, Puza Rani Sarkar, Md Ashiqul Islam, Wasik Ahmmed Fahim, and Tania Khatun. "Examining The Risk Factors of Liver Disease: A Machine Learning Approach." In 2022 7th International Conference on Communication and Electronics Systems (ICCES), pp. 1249-1257. IEEE, 2022. doi: 10.1109/ICCES54183.2022.9835732.
  28. Singh, Jagdeep, Sachin Bagga, and Ranjodh Kaur. "Software-based prediction of liver disease with feature selection and classification techniques." Procedia Computer Science 167 (2020): 1970-1980. https://doi.org/10.1016/j.procs.2020.03.226
  29. Adil, Syed Hasan, Mansoor Ebrahim, Kamran Raza, Syed Saad Azhar Ali, and Manzoor Ahmed Hashmani. "Liver patient classification using logistic regression." In 2018 4th International Conference on Computer and Information Sciences (ICCOINS), pp. 1-5. IEEE, 2018.
  30. Rohini A. Bhusnurmath and Shivaleela Betageri. “Performance Comparison of Machine Learning and Deep Learning Algorithms for Liver Disease Detection.” 8th International conference on “Emerging research in computing, information, communication and application-ERCICA-2023” Springer LNEE Series,2023 (in press)
  31. Dietterich, Thomas G. "Ensemble methods in machine learning." In Multiple Classifier Systems: First International Workshop, MCS 2000 Cagliari, Italy, June 21–23, 2000 Proceedings 1, pp. 1-15. Springer Berlin Heidelberg, 2000.
Index Terms

Computer Science
Information Sciences

Keywords

Deep ensemble stacked model Liver disease meta-classifier base-classifier Indian Liver Patient Dataset Pre-processing

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