Clinical Decision Support System for Liver Fibrosis Prediction in Hepatitis Patients: A Case Comparison of Two Soft Computing Techniques

IEEE Access

Abdeltawab Hendawi¹  Doug Young Suh²  Farman Ali³  Amjad Ali³  Shaker El-Sappagh³  Farid A. Badria⁴ 

[1] Department of Computer Science, University of Virginia, Charlottesville, VA, USA;Department of Electronics and Radio Engineering, Kyung Hee University, Yongin, South Korea;Department of Information and Communication Engineering, Inha University, Incheon, South Korea;Faculty of Pharmacy, Mansoura University, Mansoura, Egypt;

关键词: Disease diagnosis;    analytical hierarchy process;    adaptive neuro-fuzzy inference system;    clinical decision support system;    liver fibrosis detection;   

DOI  :  10.1109/ACCESS.2018.2868802

来源: DOAJ

【 摘 要 】

Diagnosis of deadly diseases, such as liver fibrosis, is very important. Clinical decision support systems (CDSSs) based on patient's historical medical data and accurate AI techniques can aid physicians in their decision-making process. The task of arriving at an accurate and timely diagnosis decision is always complex because of the dynamic, vagueness, and uncertainty associated with this disease. Fuzzy logic can perfectly handle these issues. In recent years, two of the most interesting techniques are a fuzzy analytical hierarchy process (FAHP) and an adaptive neuro-fuzzy inference system (ANFIS). The FAHP is popular for dealing with uncertainty in multi-criteria decision-making, and the ANFIS is popular in learning fuzzy inference system from data based on artificial neural networks. To the best of our knowledge, these two methods have not been used to model CDSSs in fibrosis stage detection domain. In this paper, we develop a CDSS based on a case comparison of the effectiveness of the FAHP and the ANFIS in the medical diagnosis of the fibrosis disease. We carefully design and implement two frameworks based on these two techniques. Diagnostic real data of 119 cases infected by chronic viral hepatitis C from the Liver Institute at Mansoura University in Egypt are used to train and test both the FAHP and ANFIS. Criteria and subcriteria weights are based on opinions of two domain experts. The ANFIS model is designed using trial and error based on the analysis of various experiments. Results are later compared with the diagnostic conclusions of medical expert and other three medical and fuzzy techniques. The comparison results show that these two techniques can successfully be employed in designing a diagnostic CDSS system for fibrosis diagnosis. The two techniques achieve a classification accuracy of 93.3%. The results confirm the efficiency and effectiveness of both methods. Therefore, both the FAHP and ANFIS are viable approaches in modeling CDSS for diagnosis of a liver fibrosis stage.

【 授权许可】

Unknown