An Intelligent Diagnosis of Liver Diseases using Different Decision Tree Models

Abstract

Background: Liver cancer is the third most common cause of cancer mortality. Artificial intelligence, as a diagnostic tool, can reduce physicians’ working load. However, the main fear is that due to the existence of many causes and factors, liver diseases are not easily diagnosed. This study analyzes liver disease intelligently. Various decision tree models were used in this research. Methods: The records of 583 patients in the North East of Andhra Pradesh, India, registered at the University of California in 2012, were collected. Decision tree models were compared by three measures of sensitivity, accuracy, and area under the ROC curve. Results: In this study, Decision-Stump showed better results than other models. Accuracy, sensitivity, and ROC curve of Decision-Stump were 71.3058, 1, and 0.646, respectively. Conclusion: The superior model with the highest precision is the Decision-Stump model. Therefore, the Decision-Stump model is recommended for liver disease diagnosis. This paper is invaluable for the allocation of health resources for risky people.