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@article{176657,
        author = {Sabeena S and Vivin R and Deena dhayalan N},
        title = {liver disease prediction system using machine learning techniques},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {11},
        pages = {7382-7391},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=176657},
        abstract = {Liver diseases represent a major health concern worldwide, often leading to severe outcomes if not diagnosed in time. Early detection plays a crucial role in increasing patient survival rates, yet traditional diagnostic methods can be time-consuming and resource-intensive. This paper presents a machine learning-based liver disease prediction system designed to identify the risk of liver ailments using clinical data parameters. Leveraging the UCI Liver Disorders dataset and cirrhosis data, the proposed system incorporates multiple supervised learning algorithms including Support Vector Machine (SVM), Random Forest, Logistic Regression, K-Nearest Neighbors (KNN), and Artificial Neural Networks (ANN). The model with the highest performance is selected for final deployment. A user-friendly web interface allows patients to input basic health parameters derived from routine blood tests, enabling the system to instantly predict the risk of liver disease. Experimental results demonstrate that the SVM classifier achieves the highest accuracy, reaching up to 95%, making this approach both effective and reliable for real-world healthcare applications.},
        keywords = {Liver Disease Prediction, Machine Learning, Support Vector Machine (SVM), Indian Liver Patient Dataset (ILPD), Cirrhosis, Clinical Parameters, Data Preprocessing, Supervised Learning Algorithms, Random Forest, Artificial Neural Network (ANN), Medical Diagnostics, Healthcare Technology, Early Detection, Web-based Application, Health Informatics.},
        month = {April},
        }