Hepatocellular carcinoma (HCC) is one of the most prevalent forms of primary liver cancer and requires timely identification to improve treatment outcomes. This study proposes a machine learning-based approach for detecting HCC using Logistic Regression. The proposed method analyses relevant clinical and diagnostic attributes to distinguish between patients with and without liver cancer. Data preprocessing techniques are applied to improve data quality, handle missing values, and transform input variables into a suitable format for model training. Logistic Regression is then employed to learn the relationship between the selected features and the occurrence of HCC. The trained model produces a probability-based prediction that can assist in identifying high-risk cases. Model performance can be assessed using accuracy, precision, recall, F1-score, and the area under the receiver operating characteristic curve. The proposed approach provides a simple, interpretable, and computationally efficient framework that may support early liver cancer screening and clinical decision-making.
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