Use of machine learning models for the prognostication of liver transplantation: A systematic review

Figure 1 Machine learning popularity: Worldwide popularity score of different types of machine learning algorithms. Popularity scores range from 0 (minimum) to 100 (maximum) and are plotted against the timestamp information on the x-axis. The y-axis represents the corresponding popularity score[44]. Citation: Sarker IH. Machine Learning: Algorithms, Real-World Applications and Research Directions. SN Comput Sci 2021; 2: 160. Copyright ©The Author(s) 2021. Published by Springer Nature.

Figure 2 Basic machine learning model: Process of training and testing in machine learning[44]. Citation: Sarker IH. Machine Learning: Algorithms, Real-World Applications and Research Directions. SN Comput Sci 2021; 2: 160. Copyright ©The Author(s) 2021. Published by Springer Nature.

Figure 3 PRISMA flow chart of the selection process.

Figure 4 Countries that published machine learning studies related to liver transplantation and prognosis extracted from literature over the study period.

Figure 5 Increase in machine learning studies related to liver transplantation and prognosis in the past 5 years.