Mitra Montazeri - Medical Informatics Research Center, Institute for Futures Studies in Health, Kerman University of Medical Sciences, Kerman, Iran
Mahdieh Montazeri - Medical Informatics Research Center, Institute for Futures Studies in Health, Kerman University of Medical Sciences, Kerman, Iran
Leila Ahmadian - Medical Informatics Research Center, Institute for Futures Studies in Health, Kerman University of Medical Sciences, Kerman, Iran
Mohammad Javad Zahedi - Physiology Research Center and Department of Gastroenterology, Kerman University of Medical Sciences, Kerman, Iran
Amin Beygzadeh - Assistant Professor of Medical Education, Medical Education Leadership and Management Research Center, Kerman University of Medical Sciences, Kerman, Iran

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 ۵۸۳ patients in the North East of Andhra Pradesh, India, registered at the University of California in ۲۰۱۲, 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 ۷۱.۳۰۵۸, ۱, and ۰.۶۴۶, 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.

Diagnosis, Liver disease, Decision tree models