Diagnosis of Liver Disorders Using a Combination of Adaptive Neuron-Fuzzy Inference System and Particle Swarm Optimization Algorithm

Document Type : Original Article

Authors
Mohammadhassan Ahmadi 1
Mohammadreza Ramezanpour 2
Reihaneh Khorsand 3
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
10.22122/him.v16i3.3886
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
Data Mining
Diagnosis
Liver Diseases
Patients
Heuristics
  1. Midhila M, Raghesh Krishnan K, Sudhakar R. A study of the phases of classification of liver diseases from ultrasound images and gray level difference weights based segmentation. Proceedings of the 2017 International Conference on Communication and Signal Processing (ICCSP); 2017 Apr 6-8; Chennai, India.
  2. Anisha PR, Reddy CKK, Prasad LVN. A pragmatic approach for detecting liver cancer using image processing and data mining techniques. Proceedings of the 2015 International Conference on Signal Processing and Communication Engineering Systems; 2015 Jan 2-3; Guntur, India.
  3. Abdar M. A survey and compare the performance of ibm spss modeler and rapid miner software for predicting liver disease by using various data mining algorithms. Cumhuriyet University Faculty of Science 2015; 36(3): 3230-41.
  4. Ramana BV, Babu MSP, Venkateswarlu NB. A critical study of selected classification algorithms for liver disease diagnosis. International Journal of Database Management Systems 2011; 3(2): 101-14.
  5. Maimone S, Saffioti F, Oliva G, Di Benedetto A, Alibrandi A, Filomia R, et al. Erectile dysfunction in compensated liver cirrhosis. Dig Liver Dis 2019; 51(6): 843-9.
  6. Maynard E. Decompensated cirrhosis and fluid resuscitation. Surg Clin North Am 2017; 97(6): 1419-24.
  7. Pazouki M, Sepehri MM, Saberifiroozi M. Discovering hidden cluster structures in patients with cirrhosis based on laboratory data. Govaresh 2014; 19(3): 191-7. [In Persian].
  8. Mohapatra P, Chakravarty S, Dash PK. An improved cuckoo search based extreme learning machine for medical data classification. Swarm Evol Comput 2015; 24: 25-49.
  9. Baitharu TR, Pani SK. Analysis of data mining techniques for healthcare decision support system using liver disorder dataset. Procedia Comput Sci 2016; 85: 862-70.
  10. Abdar M, Zomorodi-Moghadam M, Das R, Ting IH. Performance analysis of classification algorithms on early detection of liver disease. Expert Syst Appl 2017; 67: 239-51.
  11. Islam MM, Wu CC, Poly TN, Yang HC, Li YJ. Applications of machine learning in fatty live disease prediction. Stud Health Technol Inform 2018; 247: 166-70.
  12. Kadu G, Raut R, Gawande SS. Diagnosis of liver abnormalities using support vector machine. Journal of Science and Engineering Development Research 2018; 3(7): 132-7.
  13. Ebrahimi Khameneh M, Sepehri MM, Saberifiroozi M. Using data mining for identify patients at high risk to hepatocellular carcinoma in cirrhosis liver: Preliminary report. Govaresh 2015; 19(4): 265-74. [In Persian].
Health Information Management
Volume 16, Issue 3 - Serial Number 3
Summer 2019
Pages 115-121