Abstract
The process of identifying patterns in huge datasets comprising methods such as machine learning, statistics, and database system can be considered for data mining. It is a multidisciplinary field in computer science and it excerpts knowledge from the massive data set and converts it into comprehensible format. The Medical environment is rich in information but weak in knowledge. Medical systems contain wealth of data which require a dominant analysis tool for determining concealed association and drift in data. The health care condition that comprehends to liver disorder is termed as Liver disease. Liver disorder leads to abrupt health status like Hepatocellular Carcinoma (HCC) that precisely governs the working of liver and intern affecting other organs in the body. Machine learning techniques can be used to get the result of a test with indistinguishable degree of accuracy. Data mining classification techniques like Decision Tree, Support Vector Machine Fine Gaussian and Linear Discriminant algorithms are applied. Laboratory parameters of the patients are used as the dataset. Data contains features that can establish a rigorous model using Classification technique. Linear Discriminant algorithm showed the highest prediction accuracy 95.8% and ROC is 0.93.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Meng, D., Zhang, L., Cao, G., Cao, W., Zhang, G., Hu, B.: Liver fibrosis classification based on transfer learning and FCNet for ultrasound images. IEEE Access 5, 2169–3536 (2017)
Sontakke, S., Lohokare, J., Dani, R.: Diagnosis of liver diseases using machine learning. In: 2017 International Conference on Emerging Trends & Innovation in ICT (ICEI) Pune Institute of Computer Technology, Pune, India, 3–5 February 2017
Saha, S., Saha, S., Bhattacharya, P.P., Subhash, N.: Classifier Fusion for Liver Function Test Based Indian Jaundice Classification. In: 2015 International Conference on Man and Machine Interfacing (MAMI). IEEE (2015). 978-1-5090-0225-2/15
Singh, A., Pandey, B.: Classification of primary biliary cirrhosis using hybridization of dimensionality reduction and machine learning methods. In: 2016 International Conference on Inventive Computation Technologies (ICICT) (2016). 978- 1-5090-1285-5, 7823232
Singh, A., Pandey, B.: An euclidean distance based KNN computational method for assessing degree of liver damage. In: 2016 International Conference on Inventive Computation Technologies (ICICT) (2016). 7823222
Karthick, R., Malathi, A.: Preprocessing of various data sets using different classification algorithms for evolutionary programming. Int. J. Sci. Res. (IJSR) 4(4), 2730–2733 (2015)
Dixon, S., Yu, X.H.: Liver disorder detection based on artificial immune systems. In: 2015 11th International Conference on Natural Computation (ICNC) (2015). 978-1-4673-7679-2/15/$31.00
Song, Y.Y., Lu, Y.: Decision tree methods: applications for classification and prediction. Shanghai Arch. Psychiatry 27(2), 130 (2015)
Arutchelvan, K., Periyasamy, R.: Cancer prediction system using data mining techniques. Int. Res. J. Eng. Technol. (IRJET) 02(08), 1179–1183 (2015)
Bahramirad, S., Mustapha, A., Eshraghi, M.: Classification of Liver Disease Diagnosis: A Comparative Study (2013). ISBN: 978-1-4673-5256-7/13/$31.00
Hall, P., Cash, J.: What is the real function of the liver ‘function’ tests? Ulster Med. J. 81(1), 30–36 (2012)
McLernon, D.J., Dillon, J.F., Sullivan, F.M., Roderick, P., Rosenberg, W.M., Ryder, S.D., Donnan, P.T.: The utility of liver function tests for mortality prediction within one year in primary care using the algorithm for liver function investigations (ALFI). PLoS ONE 7(12), e50965 (2012)
Kurt, I., Ture, M., Kurum, A.T.: Comparing performances of logistic regression, classification and regression tree, and neural networks for predicting coronary artery disease. 0957-4174/$ - see front matter 2006 Elsevier Ltd (2008)
Dreiseitla, S., Ohno-Machado, L.: Logistic regression and artificial neural network classification models: a methodology review. 1532-0464/02/$ - see front matter 2003 Elsevier Science (USA) (2002)
Kim, S., Jung, S., Park, Y., Lee, J., Park, J.: Effective liver cancer diagnosis method based on machine learning algorithm. In: 2014 7th International Conference on Biomedical Engineering and Informatics (2014). 978-1-4799-5838-2 2014
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Gogi, V.J., Vijayalakshmi, M.N. (2020). Analysis of Liver Disease and HCC Inducing Factors Using Machine Learning Algorithms. In: Hemanth, D., Shakya, S., Baig, Z. (eds) Intelligent Data Communication Technologies and Internet of Things. ICICI 2019. Lecture Notes on Data Engineering and Communications Technologies, vol 38. Springer, Cham. https://doi.org/10.1007/978-3-030-34080-3_59
Download citation
DOI: https://doi.org/10.1007/978-3-030-34080-3_59
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-34079-7
Online ISBN: 978-3-030-34080-3
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)