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Is your feature request related to a problem? Please describe.
Liver cirrhosis is an irreversible condition characterized by the scarring of liver tissue, often leading to liver failure if not managed effectively. Due to the critical need for early intervention, there is a high demand for reliable predictive models that can assist in diagnosing cirrhosis based on readily available clinical data. This project aims to develop and evaluate machine learning models to predict liver cirrhosis accurately, enabling healthcare professionals to make timely decisions for better patient outcomes.
Describe the solution you'd like
This project focuses on predicting liver cirrhosis in patients based on a variety of clinical and laboratory parameters. The dataset contains features such as patient age, gender, bilirubin levels, protein counts, liver enzymes, and the albumin-globulin ratio. Machine learning techniques, including logistic regression, decision trees, and artificial neural networks (ANNs), are applied for classification. The models are evaluated on metrics like accuracy, sensitivity, and specificity to ensure robust prediction capabilities for liver cirrhosis, which is crucial for early diagnosis and management.