Recent Developments In Machine Learning Approach For Liver Disease Prediction

Abstract

Purpose: The purpose of this study is to investigate the progress and utilisation of machine learning and deep learning in forecasting liver diseases through the analysis of health records related to patients. Methods: The paper focuses on the methodology and basic framework employed for predicting and diagnosing liver disorders using artificial intelligence. It discusses the use of multiple sources of patient data, including demographic information, clinical records, and medical images. The difficulties encountered in constructing these models and the approaches used to overcome them are also highlighted.

Results: The research evaluates the performance of the algorithms used in predicting liver diseases and provides insights into their advantages and disadvantages. It demonstrates that when multiple sources of patient data are available, artificial intelligence can achieve high accuracy in predicting and diagnosing liver disorders.

Conclusion: The paper concludes that advances in machine learning have significantly contributed to the prediction of liver diseases. The use of artificial intelligence with multiple patient data sources enables accurate diagnosis and prediction. However, challenges in model construction and algorithm performance evaluation need to be addressed. The research highlights the potential future directions in this field and emphasizes the prospective impact on the medical industry.