Abstract
The rapid growth in count of patients suffering from liver disease is a major concern all over the globe. Identification of persons having liver disease is done through liver biopsy and by visual checking of MRI by trained experts which is a tedious and time-consuming process. Therefore, there is a need to develop automated diagnosis system which can provide results in less time and with high accuracy. Researchers worked on this domain and came up with various models for detection of liver disease and its severity using machine learning algorithms. This paper presents a systematic and comprehensive review of the work done in this domain focusing on various machine learning techniques developed by various authors for prediction of liver disease. The performance comparison of the various algorithms is also discussed. This study also explores the datasets used by the various authors for liver disease prediction. Finally, in the conclusion section the challenges involved in liver disease prediction and future scope is discussed.
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Singh, G., Agarwal, C., Gupta, S. (2022). Detection of Liver Disease Using Machine Learning Techniques: A Systematic Survey. In: Balas, V.E., Sinha, G.R., Agarwal, B., Sharma, T.K., Dadheech, P., Mahrishi, M. (eds) Emerging Technologies in Computer Engineering: Cognitive Computing and Intelligent IoT. ICETCE 2022. Communications in Computer and Information Science, vol 1591. Springer, Cham. https://doi.org/10.1007/978-3-031-07012-9_4
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