Published Paper Details:

Machine learning has a great potential in healthcare industry in various tasks ranging from diagnosis to decision making. There are several machine learning algorithms available which are suitable for various tasks but selecting the best one is a challenge. For selecting the best algorithm, we conducted a study on supervised machine learning algorithms for detecting liver disease in patients. In this paper, we have discussed various supervised machine learning algorithms and analyzed performance of those algorithms for liver disease detection for Indian liver patient records. To implement the algorithms, Indian liver patient records data set was used with 583 instances with 10 attributes as independent variables and one as dependent variable for the analysis. From this research study, the results show that SVM gives the best results in liver disease detection as compared to Logistic Regression, K-Nearest Neighbours, Naive Bayes (NB), Random Forest and Neural Network algorithm.