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@article{156200,
        author = {Vishnu Teja S Hingoli and Narendra G and Tejas SV and Predeep E  and Narayana H M},
        title = {prediction and diagnosis of liver disease using machine learning models },
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {9},
        number = {2},
        pages = {1134-1138},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=156200},
        abstract = {Liver disease is one of the key causes of high numbers of deaths in the country and is considered a life-threatening disease, not just anywhere, but worldwide. Liver disease can also impact peoples early in their life. More than 2.4 percent of annual Indian deaths are due to liver disorders. It is also difficult to detect liver disease due to mild symptoms in the early stages. If it is too late the signs always come to light. Thus liver-related disease poses more problems for people living and is more important nowadays to recognize the causes, and identification phase. So, for early detection of liver disease, an automated program is needed to build with more accuracy and reliability. Specific machine learning models are developed for this purpose to predict the disease. In this paper, the methods of Support Vector Machines (SVM), Decision Tree (DT) Neural Network and Random Forest (RF) is proposed to predict liver disease with better precision, accuracy and reliability.},
        keywords = {Mean Square Error (MSE), Mean Absolute Error (MAE), R-Squared Error, Root Mean Square Error (RMSE)},
        month = {},
        }