Smart Model to Distinguish Crohn’s Disease from Ulcerative Colitis
Inflammatory bowel diseases (IBD) is a term referring to chronic and recurrent gastrointestinal disease. It includes Crohn’s disease (CD) and ulcerative colitis (UC). It is undeniable that presenting features may be unclear and do not enable differentiation between disease types. Therefore...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2019-04-01
|
| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/9/8/1650 |
| _version_ | 1818844010830102528 |
|---|---|
| author | Anna Kasperczuk Jaroslaw Daniluk Agnieszka Dardzinska |
| author_facet | Anna Kasperczuk Jaroslaw Daniluk Agnieszka Dardzinska |
| author_sort | Anna Kasperczuk |
| collection | DOAJ |
| description | Inflammatory bowel diseases (IBD) is a term referring to chronic and recurrent gastrointestinal disease. It includes Crohn’s disease (CD) and ulcerative colitis (UC). It is undeniable that presenting features may be unclear and do not enable differentiation between disease types. Therefore, additional information, obtained during the analysis, can definitely provide a potential way to differentiate between UC and CD. For that reason, finding the optimal logistic model for further analysis of collected medical data, is a main factor determining the further precisely defined decision class for each examined patient. In our study, 152 patients with CD or UC were included. The collected data concerned not only biochemical parameters of blood but also very subjective information, such as data from interviews. The built-in logistics model with very high precision was able to assign patients to the appropriate group (sensitivity = 0.84, specificity = 0.74, AUC = 0.93). This model indicates factors differentiating between CD and UC and indicated odds ratios calculated for significantly different variables in these two groups. All obtained parameters of the model were checked for statistically significant. The constructed model was able to be distinguish between ulcerative colitis and Crohn’s disease. |
| first_indexed | 2024-12-19T05:06:58Z |
| format | Article |
| id | doaj.art-780c6a05a8ff41e7a75efb1709eafee7 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-12-19T05:06:58Z |
| publishDate | 2019-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-780c6a05a8ff41e7a75efb1709eafee72022-12-21T20:34:54ZengMDPI AGApplied Sciences2076-34172019-04-0198165010.3390/app9081650app9081650Smart Model to Distinguish Crohn’s Disease from Ulcerative ColitisAnna Kasperczuk0Jaroslaw Daniluk1Agnieszka Dardzinska2Department of Biocybernetics and Biomedical Engineering, Bialystok University of Technology, Wiejska 45c, 15-351 Bialystok, PolandDepartment of Gastroenterology and Internal Medicine, Medical University of Bialystok, M. Sklodowskiej-Curie 24a, 15-276 Bialystok, PolandDepartment of Biocybernetics and Biomedical Engineering, Bialystok University of Technology, Wiejska 45c, 15-351 Bialystok, PolandInflammatory bowel diseases (IBD) is a term referring to chronic and recurrent gastrointestinal disease. It includes Crohn’s disease (CD) and ulcerative colitis (UC). It is undeniable that presenting features may be unclear and do not enable differentiation between disease types. Therefore, additional information, obtained during the analysis, can definitely provide a potential way to differentiate between UC and CD. For that reason, finding the optimal logistic model for further analysis of collected medical data, is a main factor determining the further precisely defined decision class for each examined patient. In our study, 152 patients with CD or UC were included. The collected data concerned not only biochemical parameters of blood but also very subjective information, such as data from interviews. The built-in logistics model with very high precision was able to assign patients to the appropriate group (sensitivity = 0.84, specificity = 0.74, AUC = 0.93). This model indicates factors differentiating between CD and UC and indicated odds ratios calculated for significantly different variables in these two groups. All obtained parameters of the model were checked for statistically significant. The constructed model was able to be distinguish between ulcerative colitis and Crohn’s disease.https://www.mdpi.com/2076-3417/9/8/1650logistic modeldiagnosticinflammatory bowel diseasesCrohn’s diseaseulcerative colitis |
| spellingShingle | Anna Kasperczuk Jaroslaw Daniluk Agnieszka Dardzinska Smart Model to Distinguish Crohn’s Disease from Ulcerative Colitis Applied Sciences logistic model diagnostic inflammatory bowel diseases Crohn’s disease ulcerative colitis |
| title | Smart Model to Distinguish Crohn’s Disease from Ulcerative Colitis |
| title_full | Smart Model to Distinguish Crohn’s Disease from Ulcerative Colitis |
| title_fullStr | Smart Model to Distinguish Crohn’s Disease from Ulcerative Colitis |
| title_full_unstemmed | Smart Model to Distinguish Crohn’s Disease from Ulcerative Colitis |
| title_short | Smart Model to Distinguish Crohn’s Disease from Ulcerative Colitis |
| title_sort | smart model to distinguish crohn s disease from ulcerative colitis |
| topic | logistic model diagnostic inflammatory bowel diseases Crohn’s disease ulcerative colitis |
| url | https://www.mdpi.com/2076-3417/9/8/1650 |
| work_keys_str_mv | AT annakasperczuk smartmodeltodistinguishcrohnsdiseasefromulcerativecolitis AT jaroslawdaniluk smartmodeltodistinguishcrohnsdiseasefromulcerativecolitis AT agnieszkadardzinska smartmodeltodistinguishcrohnsdiseasefromulcerativecolitis |