Decision factors on effective liver patient data prediction

Abstract

Various types of stress and irregular eating habits, as well as inhalation of alcohol and ongoing toxic gas, ingestion of contaminated food, excessive consumption of pickled food and drug intake, enables liver disease patients to grow up year by year. To this end, variety of data mining algorithms can help medical doctors in diagnostics of patients at the hospital. This paper treats an evaluation of the analyzed results of classification algorithms selected for better prediction based on the characteristics of data from the data set with liver disease. We investigated and analyzed the classification algorithms such as Naïv e Bayes, Decision Tree, Multi-Layer Perceptron and k-NN used in a previous study, which developed our data set, and additionally Random forest, Logistic which proposed by us. Those algorithms were compared in several kinds of evaluating criteria like precision, recall, sensitivity, specificity, and so on. Through the experiments, we could know that in view of precision, Naïv e Bayes is preferable than others, but in other criteria such as Recall and Sensitivity, Logistic and Random Forest took precedence over other algorithms in the performance of prediction test as considering the algorithmic characteristics to liver patient data set. © 2014 SERSC.