| Abstrakt: |
Liver disease continues to remain a global impairment in health. Millions of people are taken ill with cirrhosis, hepatitis, or liver cancer all over the world. The early diagnosis and timely intervention are essential for better outcomes of patients, making prediction modeling the first step across the spectrum of modern medicine. The promising and developing field of ML and DL technologies has now provided new opportunities to enhance the accuracy of predictions and efficiency in their execution in the case of liver disease. This work attempts an exploration into employing ML and DL techniques for predicting liver disease from several clinical datasets. We, therefore, discuss the incidence and impact of liver disease and highlight the need for innovative diagnosis approaches. We then elaborate further on the fundamental ideas that support some selective ML algorithms, such as decision tree, random forests, and support vector machines, which have a great place in exploring structured data. Models such as these are very suitable to interpret the relationships between various clinical parameters and liver health, facilitating early detection and therapy suggestions. Then data quality, availability, and the need for large annotated datasets to train DL models are discussed. Ethical considerations regarding patient data privacy and model interpretability are mentioned, emphasizing the imperative for transparency in the development of AI systems for healthcare. It exemplifies that integration of algorithms and certifies the potential for diagnostics and prognostics through the Hybrid Model, which has the power to consider patient stratifications. [ABSTRACT FROM AUTHOR] |
