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Abstract
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The necessity and significance of researching predict Chronic Liver Disease (CLD) patients using machine learning and deep learning algorithms are highlighted by the ongoing demand for more accurate diagnostics and personalized therapeutic approaches. Recent studies have showcased ML and DL's potential to enhance disease progression predictions, identify high-risk individuals, and tailor treatment plans to patient-specific data. By analyzing complex datasets from imaging, genomics, and clinical parameters, these advanced computational techniques enable early detection of liver fibrosis and cirrhosis, crucial for improving patient outcomes. Moreover, the usage of ML and DL in clinical environments supports real-time monitoring and decision-making, leading to better patient management and reduced healthcare expenses. Therefore, exploring this technology within CLD research is vital for advancing the understanding and treatment of this widespread disease.
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