Document Type : Original Article
Authors
1 MSc Student, Computer Engineering, Department of Computer Engineering, Mobarakeh Branch, Islamic Azad University, Isfahan, Iran
2 Assistant Professor, Computer Engineering, Department of Computer Engineering, Mobarakeh Branch, Islamic Azad University, Isfahan, Iran
3 Assistant Professor, Computer Engineering, Department of Computer Engineering, Dolatabad Branch, Islamic Azad University, Isfahan, Iran
Abstract
Introduction: The incidence of liver diseases in a person can lead to susceptibility to liver cancer in long-term, which is one of the deadliest forms of cancer in the world, which can be prevented. Early diagnosis of liver diseases is essential for their treatment. The purpose of this study was to classify the status of liver patients based on laboratory parameters using the data mining approach.Methods: In this descriptive study, particle swarm optimization (PSO) algorithms and adaptive neuron-fuzzy inference system (ANFIS) were used to diagnose liver disorders in healthy individuals and patients. The data were taken from University of California-Irvine (UCI) database. Accuracy, sensitivity, and precision criteria were used to evaluate the proposed method.Results: The combination of ANFIS and PSO algorithm with average accuracy of 14.99 percent was able to detect liver disorders in Indian Liver Patient Dataset (ILPD).Conclusion: The results of this study indicate the high abilities of ANFIS in liver disorders detection. The proposed model has minimum error, and maximum accuracy and precision compared to other models. The application of this model is proposed in the detection of liver diseases.
Keywords
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