Clinical Decision Support System for Liver Fibrosis Prediction in Hepatitis Patients: A Case Comparison of Two Soft Computing Techniques
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 diag...
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2018-01-01
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| author | Shaker El-Sappagh Farman Ali Amjad Ali Abdeltawab Hendawi Farid A. Badria Doug Young Suh |
| author_facet | Shaker El-Sappagh Farman Ali Amjad Ali Abdeltawab Hendawi Farid A. Badria Doug Young Suh |
| author_sort | Shaker El-Sappagh |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-b6060701280a4fcebbc4e49fb63a965c |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2018-01-01 |
| publisher | IEEE |
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| series | IEEE Access |
| spelling | doaj-art-b6060701280a4fcebbc4e49fb63a965c2025-08-20T02:40:13ZengIEEEIEEE Access2169-35362018-01-016529115292910.1109/ACCESS.2018.28688028470076Clinical Decision Support System for Liver Fibrosis Prediction in Hepatitis Patients: A Case Comparison of Two Soft Computing TechniquesShaker El-Sappagh0https://orcid.org/0000-0001-9705-1477Farman Ali1https://orcid.org/0000-0002-9420-1588Amjad Ali2Abdeltawab Hendawi3Farid A. Badria4Doug Young Suh5Department of Information and Communication Engineering, Inha University, Incheon, South KoreaDepartment of Information and Communication Engineering, Inha University, Incheon, South KoreaDepartment of Information and Communication Engineering, Inha University, Incheon, South KoreaDepartment of Computer Science, University of Virginia, Charlottesville, VA, USAFaculty of Pharmacy, Mansoura University, Mansoura, EgyptDepartment of Electronics and Radio Engineering, Kyung Hee University, Yongin, South KoreaDiagnosis 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.https://ieeexplore.ieee.org/document/8470076/Disease diagnosisanalytical hierarchy processadaptive neuro-fuzzy inference systemclinical decision support systemliver fibrosis detection |
| spellingShingle | Shaker El-Sappagh Farman Ali Amjad Ali Abdeltawab Hendawi Farid A. Badria Doug Young Suh Clinical Decision Support System for Liver Fibrosis Prediction in Hepatitis Patients: A Case Comparison of Two Soft Computing Techniques IEEE Access Disease diagnosis analytical hierarchy process adaptive neuro-fuzzy inference system clinical decision support system liver fibrosis detection |
| title | Clinical Decision Support System for Liver Fibrosis Prediction in Hepatitis Patients: A Case Comparison of Two Soft Computing Techniques |
| title_full | Clinical Decision Support System for Liver Fibrosis Prediction in Hepatitis Patients: A Case Comparison of Two Soft Computing Techniques |
| title_fullStr | Clinical Decision Support System for Liver Fibrosis Prediction in Hepatitis Patients: A Case Comparison of Two Soft Computing Techniques |
| title_full_unstemmed | Clinical Decision Support System for Liver Fibrosis Prediction in Hepatitis Patients: A Case Comparison of Two Soft Computing Techniques |
| title_short | Clinical Decision Support System for Liver Fibrosis Prediction in Hepatitis Patients: A Case Comparison of Two Soft Computing Techniques |
| title_sort | clinical decision support system for liver fibrosis prediction in hepatitis patients a case comparison of two soft computing techniques |
| topic | Disease diagnosis analytical hierarchy process adaptive neuro-fuzzy inference system clinical decision support system liver fibrosis detection |
| url | https://ieeexplore.ieee.org/document/8470076/ |
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