Hepatitis C poses a significant public health challenge, often progressing to life-threatening chronic infections without early symptoms, making it difficult to detect and diagnose This study promptly provides a solution by developing a machine learning model aimed at the early detection of hepatitis C-related liver diseases A comprehensive analysis was done using data from the captured UCI Machine Learning Repository, with laboratory internal values and patient demographics and apply 5 algorithms for liver diseases classification. The study's results were good, achieving a validation accuracy of 97% and testing accuracy of 95%. For detailed analysis, we used a confusion matrix to reveal additional insights into the model performance, as it achieved a Precision score of 99%, Recall score of 99%, and F1-score of 99% as well. These findings present the promising potential of machine learning for early disease classification, which can help in medical applications, minimize human error, and participate in improved detection of Liver diseases.
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