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@article{168738,
        author = {SYED SHAZIA and B MURALI},
        title = {Analysis of Liver Disease Prediction Using Machine Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2024},
        volume = {11},
        number = {},
        pages = {1937-1942},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=168738},
        abstract = {Liver disease is becoming one of the most fatal diseases in several countries. Patients with the Liver disease have been continuously increasing because of excessive consumption of alcohol, inhaling of harmful gases, intake of contaminated food, pickles, and drugs. These days, AI strategies have generally been utilized in clinical science for guaranteeing precision. In this work, we have precisely built computational model structure procedures for liver infection forecast. We utilized some effective characterization calculations (Decision Tree, ANN and Support Vector Machine) for chronic liver disease patients which lasts over six months. We proposed an investigation model to predict liver infection with a high exactness value. Then, we analysed the good and bad values using a machine learning classifier which improvises the classification resultant. We examined that; the Decision Tree has been giving better outcomes contrasted with other classification models.},
        keywords = {Liver Disease, Chronic Liver Disease (CLD), Artificial Intelligence (AI) in Healthcare, Machine Learning (ML) in Medicine, Liver Disease Prediction, Computational Modeling, Classification Algorithms, Decision Tree, Artificial Neural Network (ANN), Support Vector Machine (SVM)},
        month = {October},
        }