A Survey on Predicting Advanced Liver Fibrosis Using Different Machine Learning Algorithms
DOI:
https://doi.org//10.32628/IJSRSET207138Keywords:
Liver Fibrosis, Hepatitis C, Machine LearningAbstract
Machine learning (ML) is a subsection of AI. The goal of ML is to understand the structure of data and fit that data into models that can be used for prediction, classification etc. Although machine learning is an area within computer science, it differs from traditional computational approaches. In recent years, different machine learning algorithms are used for disease prediction. Algorithms like Decision Tree (DT), Support Vector Machine (SVM), Particle Swarm Optimization (PSO), Multi- Linear Regression, Random Forest, Genetic Algorithm (GA), Artificial Neural Network (ANN), Naive Bayes, etc. are used for classification. Using these algorithms liver fibrosis stages can be predicted. This paper discusses different machine learning algorithms for the prediction of liver fibrosis stage and the performance analysis of these algorithms in various studies.
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