ABSTRACT
A good and healthy life is the basic right of every human being. To achieve that, it is needed to make available better and cheaper medical facilities to everyone. There are a lot of disease a human life suffers with. Our focus in this paper is to study and analyze various classifiers algorithms to predict various liver diseases. We have compared the performance of Random Forest, Logistic Regression, Support Vector Machine, Decision Tree, Reduction Rule, Naive Bayes, and K-Nearest Neighbors classification algorithms. We have utilized an Irvine database of the University of California, namely the Indian Liver Patient Dataset (ILPD). The various characteristics of a patient such as gender, age, total bilirubins, direct bilirubins, albumins, globulin ratios, alkphos, sgot and sgpt were evaluated. The implementation of classification algorithms is done on the rapid miner tool along with Liver Patient dataset and comparison is done on the parameters such as training time, scoring time, accuracy, precision. The result of other parameters like classification error, recall is also compared on liver patient diseases dataset. We also proposed the use of a radial kernel function as a support vector machine, and it has observed that true prediction of class1 is about 81.30% having class recall is 96.15% and class2 is about 82.22% having class recall is 44.31%.
