Published Paper Details:

Liver Failure is a serious condition and it affects the patients life time. Disease identification is the most crucial. Al task for treating any disease. Liver disease can be inherited (genetic) or caused by a variety of factors that damage the liver. Machine learning technique is widely used in various fields of science and technology. They have been giving out meaningful and classified information. It also explores in constructing and study of algorithms which can learn from data. Data mining in healthcare is an emerging field of high importance for providing prognosis and a deeper understanding of medical data. Building an effective disease management strategy requires analysis of large amount of data, early detection of the disease, assessment of the severity and early prediction of adverse events. This will inhibit the progression of the disease, will improve the quality of life of the patients and will reduce the associated medical costs. Toward this direction machine learning techniques have been employed. The aim of this paper is to present the state-of-the-art of the machine learning methodologies applied for the valuation of liver failure. The main objective of this research work was to find the best classification algorithm in terms of precision, accuracy, specificity and sensitivity. Therefore, the present investigation was done to determine the relative performance of four classification algorithms namely, Support Vector machine (SVM), Logistic Regression, Random Forest and Decision Tree algorithm based on collected liver patients data.