Study on the Application of Serological Indexes Based on Machine Learning in Non-invasive Detection of Liver Fibrosis

This study aims to develop a non-invasive detection method for liver fibrosis using machine learning, predicting the serological markers of patients with liver fibrosis. The study first screened out characteristic variables through data preprocessing and feature selection techniques, then established a prediction model for the serological markers of liver fibrosis patients using machine learning methods such as support vector regression, logistic regression, and random forest. Ultimately, a non-invasive detection model for liver fibrosis based on machine learning was developed. The study results indicate that this model can effectively diagnose liver fibrosis non-invasively and screen patients with liver fibrosis. Using machine learning to select serological markers significantly improves the accuracy of liver fibrosis diagnosis, providing a new approach for non-invasive detection based on serological markers.