Title of article :
An Intelligent Diagnosis of Liver Diseases using Different Decision Tree Models
Author/Authors :
Montazeri ، Mitra Medical Informatics Research Center, Institute for Futures Studies in Health - Kerman University of Medical Sciences , Montazeri ، Mahdieh Health Information Sciences Department - Faculty of Management and Medical Information Sciences - Kerman University of Medical Sciences , Ahmadian ، Leila Medical Informatics Research Center, Institute for Futures Studies in Health - Kerman University of Medical Sciences , Zahedi ، Mohammad Javad Department of Gastroenterology - Physiology Research Center - Kerman University of Medical Sciences , Beigzadeh ، Amin Sirjan school of Medical Sciences
From page :
113
To page :
116
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.
Keywords :
Diagnosis , Liver disease , Decision tree models
Journal title :
Journal of Kerman University of Medical Sciences (JKMU)
Journal title :
Journal of Kerman University of Medical Sciences (JKMU)
Record number :
2778281
Link To Document :
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