Abstract
Mining meaningful information from huge medical datasets is a key aspect of automated disease diagnosis. In recent years, liver disease has emerged as one of the commonly occurring diseases across the world. In this paper, a Convolutional Neural Network (CNN) based model is proposed for the identification of liver disease. Furthermore, the performance of CNN was also compared with traditional machine learning approaches, which include Naive Bayes (NB), Support Vector Machine (SVM), K-nearest Neighbors (KNN), and Logistic Regression (LR). For evaluation, two datasets were used: BUPA and ILPD. The experimental results showed that CNN was effective for the classification of liver disease, which produced an accuracy of 75.55%, and 72.00% on the BUPA and ILPD datasets, respectively.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Edginton, A.N., Ritter, L.: Predicting plasma concentrations of bisphenol a in children younger than 2 years of age after typical feeding schedules, using a physiologically based toxicokinetic model. Environ. Health Perspect. 117(4), 645–652 (2009)
Pandey, B., Singh, A.: Intelligent techniques and applications in liver disorders. Survey, January (2014)
Takkar, S., Singh, A., Pandey, B.: Application of machine learning algorithms to a welldefined clinical problem: liver disease. Int. J. E-Health Med. Commun. 8(4), 38–60 (2020)
Motwani, M., et al.: Machine learning for prediction of all-cause mortality in patients with suspected coronary artery disease: a 5-year multicentre prospective registry analysis. Eur. Heart J. 38, 500–507 (2016)
Sani, A.: Machine Learning for Decision Making, Université de Lille 1 (2015)
Raghupathi, W., Raghupathi, V.: Big data analytics in healthcare: promise and potential. Health Inf. Sci. Syst. 2 (2014) https://doi.org/10.1186/2047-2501-2-3
Groves, P., Kayyali, B., Knott, D., Kuiken, S.V.: The’ Big Data’ Revolution in Healthcare: Accelerating Value and Innovation (2016)
Arel, I., Rose, D.C., Karnowski, T.P.: Deep machine learninga new frontier in artificial intelligence research. IEEE Comput. Intell. Mag. 5(4), 13–18 (2010)
Rajeswari, P., Sophia Reena, G.: Analysis of liver disorder using data mining algorithm. Global J. Comput. Sci. Technol. 10(14), 48–52 (2010)
Ramana, B.V., Surendra Prasad Babu, M., Venkateswarlu, N.B.: A critical study of selected classification algorithms for liver disease diagnosis. Int. J. Database Manage. Syst. 3(2), 101–114 (2011)
Alfisahrin, S.N.N., Mantoro, T.: Data mining techniques for optimization of liver disease classification. In: 2013 International Conference on Advanced Computer Science Applications and Technologies. IEEE (2013)
Aneeshkumar, A.S., Jothi Venkateswaran, C.: A novel approach for Liver disorder Classification using Data Mining Techniques. Eng. Sci. Int. J. 2(1), 15–18(2015)
Sug, H.: Improving the prediction accuracy of liver disorder disease with oversampling. Appl. Mat. Electr. Comput. Eng. 7, 331–335 (2012)
Olaniyi, E.O., Adnan, K.: Liver disease diagnosis based on neural networks. In: Advances in Computational Intelligence, pp. 48–53 (2013)
Vijayarani, S., Dhayanand, S.: Liver disease prediction using SVM and Naïve Bayes algorithms. Int. J. Sci. Eng. Technol. Res. (IJSETR) 4(4), 816–820 (2015)
Olaniyi, E.O., Aadnan, K.: Liver disease diagnosis based on neural networks. In: Advances in Computational Intelligence, Proceedings of the 16th International Conference on Neural Networks (NN 2015), November 7–9 (2015)
UCI machine learning database. ftp://ftp.ics.uci.edu/pub/machinelearning-databases. Accessed 2020
Lichman, M.: UCI Machine Learning Repository. http://archive.ics.uci.edu/ml/datasets.html. University of California, School of Information and Computer Science, Irvine, CA (2013)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)
Bramer, M.: Principles of Data Mining, 2nd edn. Springer, London (2013). https://doi.org/10.1007/978-1-4471-4884-5
Gorunescu, F.: Data Mining Concepts, Models and Techniques. Springer, Berlin (2011). https://doi.org/10.1007/978-3-642-19721-5
Witten, I.H., Frank, E., Hall, M.A.: Data Mining Practical Machine Learning Tools and Techniques, 3rd edn. Morgan Kaufmann, United States (2011)
Wu, X.: Top 10 algorithms in data mining. Knowl. Inf. Syst. 14(1), 1–37 (2008)
Dreiseitl, S., Ohno-Machado, L.: Logistic regression and artificial neural network classification models: a methodology review. J. Biomed. Inform. 35(5–6), 352–359 (2002)
Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 Springer Nature Switzerland AG
About this paper
Cite this paper
Mutlu, E.N., Devim, A., Hameed, A.A., Jamil, A. (2022). Deep Learning for Liver Disease Prediction. In: Djeddi, C., Siddiqi, I., Jamil, A., Ali Hameed, A., Kucuk, Ä°. (eds) Pattern Recognition and Artificial Intelligence. MedPRAI 2021. Communications in Computer and Information Science, vol 1543. Springer, Cham. https://doi.org/10.1007/978-3-031-04112-9_7
Download citation
DOI: https://doi.org/10.1007/978-3-031-04112-9_7
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-04111-2
Online ISBN: 978-3-031-04112-9
eBook Packages: Computer ScienceComputer Science (R0)