Machine Learning-Based Predictive Modeling for Early Detection of Liver Cirrhosis

Abstract

Liver cirrhosis, a long-term disease of the liver that causes scarring and liver problems, is still a major health problem around the world. Early identification is very important for better patient results and lowering the cost of healthcare. This study shows a way to find liver scarring early on using predictive modeling based on machine learning. We used a large sample with demographic, clinical, and test data from people with liver disease in different stages. To make prediction models, different machine learning methods were used, such as decision trees, random forests, support vector machines, and neural networks. A strong cross-validation method was used to train and test these models to make sure they can be used in other situations and to avoid overfitting. Feature selection methods were used to find the most useful predictions, which made the model easier to understand and better at its job. The model that did the best had high accuracy, sensitivity, and specificity, showing that it could be used to reliably find liver scarring early on. We also checked how well the model could predict things by looking at the area under the receiver operating characteristic curve (AUC-ROC) and precision-recall graphs. However, the results show that machine learning methods could help doctors make better decisions and act more quickly. This work shows how important it is to use advanced analytics in hospital settings so that long-term diseases like liver cirrhosis can be better managed. In the future, researchers should focus on getting outside confirmation and making real-time prediction tools that work well with healthcare systems.