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@article{189081,
        author = {Saziya Iqbal and Ahmad Talha Siddiqui},
        title = {DIAGNOSIS OF LIVER DISEASE USING MACHINE LEARNING},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {7},
        pages = {4510-4521},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=189081},
        abstract = {Liver disease causes high rates of morbidity and mortality, making it a major worldwide health problem. Even with improvements in non-invasive diagnostic methods, precise staging and diagnosis are still difficult. A potent technique for enhancing the diagnosis and prognosis of liver disease is machine learning (ML). The goal of this project is to create an ML model using a large dataset of clinical, laboratory, and imaging data from patients with liver disease. The Random Forest (RF) classifier performed the best out of all the models. The model will be trained to forecast the phases of liver illness while taking consistency in the dataset and interpretability of the model into account. The outcomes show how machine learning (ML) may improve the management of liver disease, which has implications for tailored treatment plans. The creation of this model represents a substantial advancement in the identification and management of hepatic illness, which will ultimately improve patient outcomes and lessen the cost of healthcare.},
        keywords = {Liver disease, Decision tree, K-nearest neighbors, logistic regression, Gradient boosting, random forests,CatBoost},
        month = {December},
        }