Accurate Prediction of Advanced Liver Fibrosis Using the Decision Tree Learning Algorithm in Chronic Hepatitis C Egyptian Patients
Background/Aim. Respectively with the prevalence of chronic hepatitis C in the world, using noninvasive methods as an alternative method in staging chronic liver diseases for avoiding the drawbacks of biopsy is significantly increasing. The aim of this study is to combine the serum biomarkers and cl...
Saved in:
| Main Authors: | , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2016-01-01
|
| Series: | Gastroenterology Research and Practice |
| Online Access: | http://dx.doi.org/10.1155/2016/2636390 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832552235951718400 |
|---|---|
| author | Somaya Hashem Gamal Esmat Wafaa Elakel Shahira Habashy Safaa Abdel Raouf Samar Darweesh Mohamad Soliman Mohamed Elhefnawi Mohamed El-Adawy Mahmoud ElHefnawi |
| author_facet | Somaya Hashem Gamal Esmat Wafaa Elakel Shahira Habashy Safaa Abdel Raouf Samar Darweesh Mohamad Soliman Mohamed Elhefnawi Mohamed El-Adawy Mahmoud ElHefnawi |
| author_sort | Somaya Hashem |
| collection | DOAJ |
| description | Background/Aim. Respectively with the prevalence of chronic hepatitis C in the world, using noninvasive methods as an alternative method in staging chronic liver diseases for avoiding the drawbacks of biopsy is significantly increasing. The aim of this study is to combine the serum biomarkers and clinical information to develop a classification model that can predict advanced liver fibrosis. Methods. 39,567 patients with chronic hepatitis C were included and randomly divided into two separate sets. Liver fibrosis was assessed via METAVIR score; patients were categorized as mild to moderate (F0–F2) or advanced (F3-F4) fibrosis stages. Two models were developed using alternating decision tree algorithm. Model 1 uses six parameters, while model 2 uses four, which are similar to FIB-4 features except alpha-fetoprotein instead of alanine aminotransferase. Sensitivity and receiver operating characteristic curve were performed to evaluate the performance of the proposed models. Results. The best model achieved 86.2% negative predictive value and 0.78 ROC with 84.8% accuracy which is better than FIB-4. Conclusions. The risk of advanced liver fibrosis, due to chronic hepatitis C, could be predicted with high accuracy using decision tree learning algorithm that could be used to reduce the need to assess the liver biopsy. |
| format | Article |
| id | doaj-art-8b6aa396e45d414ca1966e81f3b07790 |
| institution | Kabale University |
| issn | 1687-6121 1687-630X |
| language | English |
| publishDate | 2016-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Gastroenterology Research and Practice |
| spelling | doaj-art-8b6aa396e45d414ca1966e81f3b077902025-02-03T05:59:09ZengWileyGastroenterology Research and Practice1687-61211687-630X2016-01-01201610.1155/2016/26363902636390Accurate Prediction of Advanced Liver Fibrosis Using the Decision Tree Learning Algorithm in Chronic Hepatitis C Egyptian PatientsSomaya Hashem0Gamal Esmat1Wafaa Elakel2Shahira Habashy3Safaa Abdel Raouf4Samar Darweesh5Mohamad Soliman6Mohamed Elhefnawi7Mohamed El-Adawy8Mahmoud ElHefnawi9Informatics and Systems Department and Biomedical Informatics and Chemo Informatics Group, Engineering Research Division and Centre of Excellence for Advanced Sciences, National Research Centre, Giza, EgyptDepartment of Endemic Medicine and Hepatology, Faculty of Medicine, Cairo University, Cairo, EgyptDepartment of Endemic Medicine and Hepatology, Faculty of Medicine, Cairo University, Cairo, EgyptCommunications, Electronics and Computers Department, Faculty of Engineering, Helwan University, Cairo, EgyptInformatics and Systems Department and Biomedical Informatics and Chemo Informatics Group, Engineering Research Division and Centre of Excellence for Advanced Sciences, National Research Centre, Giza, EgyptHepatology & Endemic Medicine, Cairo University, Cairo, EgyptHepatology and Gastroenterology, Liver Unit, Cairo University, Cairo, EgyptCommunications and Computer Department, Faculty of Engineering, Modern University, Cairo, EgyptCommunications, Electronics and Computers Department, Faculty of Engineering, Helwan University, Cairo, EgyptInformatics and Systems Department and Biomedical Informatics and Chemo Informatics Group, Engineering Research Division and Centre of Excellence for Advanced Sciences, National Research Centre, Giza, EgyptBackground/Aim. Respectively with the prevalence of chronic hepatitis C in the world, using noninvasive methods as an alternative method in staging chronic liver diseases for avoiding the drawbacks of biopsy is significantly increasing. The aim of this study is to combine the serum biomarkers and clinical information to develop a classification model that can predict advanced liver fibrosis. Methods. 39,567 patients with chronic hepatitis C were included and randomly divided into two separate sets. Liver fibrosis was assessed via METAVIR score; patients were categorized as mild to moderate (F0–F2) or advanced (F3-F4) fibrosis stages. Two models were developed using alternating decision tree algorithm. Model 1 uses six parameters, while model 2 uses four, which are similar to FIB-4 features except alpha-fetoprotein instead of alanine aminotransferase. Sensitivity and receiver operating characteristic curve were performed to evaluate the performance of the proposed models. Results. The best model achieved 86.2% negative predictive value and 0.78 ROC with 84.8% accuracy which is better than FIB-4. Conclusions. The risk of advanced liver fibrosis, due to chronic hepatitis C, could be predicted with high accuracy using decision tree learning algorithm that could be used to reduce the need to assess the liver biopsy.http://dx.doi.org/10.1155/2016/2636390 |
| spellingShingle | Somaya Hashem Gamal Esmat Wafaa Elakel Shahira Habashy Safaa Abdel Raouf Samar Darweesh Mohamad Soliman Mohamed Elhefnawi Mohamed El-Adawy Mahmoud ElHefnawi Accurate Prediction of Advanced Liver Fibrosis Using the Decision Tree Learning Algorithm in Chronic Hepatitis C Egyptian Patients Gastroenterology Research and Practice |
| title | Accurate Prediction of Advanced Liver Fibrosis Using the Decision Tree Learning Algorithm in Chronic Hepatitis C Egyptian Patients |
| title_full | Accurate Prediction of Advanced Liver Fibrosis Using the Decision Tree Learning Algorithm in Chronic Hepatitis C Egyptian Patients |
| title_fullStr | Accurate Prediction of Advanced Liver Fibrosis Using the Decision Tree Learning Algorithm in Chronic Hepatitis C Egyptian Patients |
| title_full_unstemmed | Accurate Prediction of Advanced Liver Fibrosis Using the Decision Tree Learning Algorithm in Chronic Hepatitis C Egyptian Patients |
| title_short | Accurate Prediction of Advanced Liver Fibrosis Using the Decision Tree Learning Algorithm in Chronic Hepatitis C Egyptian Patients |
| title_sort | accurate prediction of advanced liver fibrosis using the decision tree learning algorithm in chronic hepatitis c egyptian patients |
| url | http://dx.doi.org/10.1155/2016/2636390 |
| work_keys_str_mv | AT somayahashem accuratepredictionofadvancedliverfibrosisusingthedecisiontreelearningalgorithminchronichepatitiscegyptianpatients AT gamalesmat accuratepredictionofadvancedliverfibrosisusingthedecisiontreelearningalgorithminchronichepatitiscegyptianpatients AT wafaaelakel accuratepredictionofadvancedliverfibrosisusingthedecisiontreelearningalgorithminchronichepatitiscegyptianpatients AT shahirahabashy accuratepredictionofadvancedliverfibrosisusingthedecisiontreelearningalgorithminchronichepatitiscegyptianpatients AT safaaabdelraouf accuratepredictionofadvancedliverfibrosisusingthedecisiontreelearningalgorithminchronichepatitiscegyptianpatients AT samardarweesh accuratepredictionofadvancedliverfibrosisusingthedecisiontreelearningalgorithminchronichepatitiscegyptianpatients AT mohamadsoliman accuratepredictionofadvancedliverfibrosisusingthedecisiontreelearningalgorithminchronichepatitiscegyptianpatients AT mohamedelhefnawi accuratepredictionofadvancedliverfibrosisusingthedecisiontreelearningalgorithminchronichepatitiscegyptianpatients AT mohamedeladawy accuratepredictionofadvancedliverfibrosisusingthedecisiontreelearningalgorithminchronichepatitiscegyptianpatients AT mahmoudelhefnawi accuratepredictionofadvancedliverfibrosisusingthedecisiontreelearningalgorithminchronichepatitiscegyptianpatients |