Identyfikatory
Warianty tytułu
Języki publikacji
Abstrakty
The article presents the process of building a logistic regression model, which aims to support the decision-making process in medicine. Currently, there is no precise diagnosis for ulcerative colitis (UC) and Crohn's disease (CD). Specialist physicians must exclude many other diseases occurring in the colon. The first goal of this study is a retrospective analysis of medical data of patients hospitalized in the Department of Gastroenterology and Internal Diseases and finding the symptoms differentiating the two analyzed diseases. The second goal is to build a system that clearly points to UC or CD, which shortens the time of diagnosis and facilitates the treatment of patients. The work focuses on building a model that can be the basis for the construction of classifiers, which are one of the basic elements in the medical recommendation system. The developed logistic regression model will define the probability of the disease and will indicate the statistically significant changes that affect the onset of the disease. The value of probability will be one of the main reasons for the decision.
Słowa kluczowe
Czasopismo
Rocznik
Tom
Strony
227--231
Opis fizyczny
Bibliogr. 21 poz., tab.
Twórcy
autor
- *Faculty of Mechanical Engineering, Department of Biocybernetics and Biomedical Engineering, Bialystok Technical University, ul. Wiejska 45C, 15-351 Bialystok, Poland a.dardzinska@pb.edu.pl, a.kasperczuk@ pb.edu.pl
autor
- Faculty of Mechanical Engineering, Department of Biocybernetics and Biomedical Engineering, Bialystok Technical University, ul. Wiejska 45C, 15-351 Bialystok, Poland
Bibliografia
- 1. Agrawal R., Srikant R. (1993), Fast algorithm for mining assocation rules, International Conference on Very Large Databases, 487–499.
- 2. Aufses A.H. (2000), The History of Surgery for Crohn`s Disease at The Mount Sinai Hospital, Mount Sinai Journal of Medicine, 67, 198–203.
- 3. Crohn B.B., Ginzburg L., Oppenheimer G.D. (1932), Regional ileitis. A pathologic and clinical entity, Journal of the American Medical Directors Association, 99, 1323–1329.
- 4. Dardzinska A. (2013), Action rules mining, Springer-Verlag, Berlin.
- 5. Dardzinska A., Romaniuk A. (2016), Mining of Frequent Action Rules, In: Ryżko D, Gawrysiak P, Kryszkiewicz M, Rybiński H. (editors), Machine Intelligence and Big Data in Industry, Studies in Big Data, Springer, Cham, 19, 87–95.
- 6. De Kruif P. (1956), Microbe hunters (In Polish: Łowcy mikrobów), Państwowy Zakład Wydawnictw Lekarskich, Warszawa.
- 7. Gürdal O., Dardzinska A. (2017), A New Approach to Clinical Medicine by Action Rules, International Journal of Development Research, 7(1), 11032–11039.
- 8. Han J., Kamber M. (2006), Data Mining: Concepts and Techniques, Morgan Kaufmann Publishers, Second Edition, 21–27.
- 9. Harrell F. (2001), Regression Modeling Strategies with Applications to Linear Models, Logistic Regression, and Survival Analysis, Springer-Verlag, New York.
- 10. Hauser W., Hoffmn J., Kuhbacher T., Raible A., Reinshagen M., Rogler G., Schreiber S., Neukamm U., Eceterski A. (2007), Crohn's disease and other inflammatory bowel diseases (In Polish:Choroba Lesniowskiego - Crohna i inne nieswoiste zapalenia jelit), Wydawnictwo Lekarskie PZWL.
- 11. Hosmer D., Lemeshow S., Sturdivant R. (2013), Applied Logistic Regression, Wiley Series in Probability and Statistics.
- 12. Jong de P., Heller G.Z., (2008), Generalized Linear Models for Insurance Data, Cambridge University Press, Cambridge.
- 13. Kasperczuk A., Dardzinska A., (2016), Comparative Evaluation of the Different Data Mining Techniques Used for the Medical Database, Acta Mechanica et Automatica, 10(3), 233–238.
- 14. Kirsner J.B. (1988), Historical aspects of inflammatory bowel disease, Journal of Clinical Gastroenterology ,10, 286–297.
- 15. Lesniowski A. (1903), A contribution to the surgery of the bowels (In Polish:Przyczynek do chirurgii kiszek), Medycyna, 31, 460–518.
- 16. Lichtarowicz A.M., Mayberry J.F. 1988, Antoni Lesniowski and his contribution to regional enteritis (Crohn`s disease), Journal of the Royal Society of Medicine, 81, 468–470.
- 17. Liu T., Moore A., Gray A., (2003) Efficient Exact k-NN and Nonparametric Classification in High Dimensions, Advances in Neural Information Processing Systems, 16, 8–13.
- 18. Olson D., Dursun D. (2008), Advanced Data Mining Techniques, Springer.
- 19. Pauk J., Dardzinska A. (2012), New method for finding rules in incomplete information systems controlled by reducts in flat feet treatment, Image Proc. and Communications Challenges, Advances in Intelligent and Soft Computing, 184, 209–214.
- 20. Powers D. (2011), Evaluation: From Precision, Recall and FMeasure to ROC, Informedness, Markedness & Correlation, Journal of Machine Learning Technologies, 2(1), 37–63.
- 21. Ras Z., Dardzinska A. (2011), From Data to Classification Rules and Action, International Journal of Intelligent Systems, Wiley, 26(6), 572–590.
Uwagi
Research was performed as a part of projects MB/WM/8/2016 and financed with use of funds for science of MNiSW. The Bioethical Commission gave permission for the analysis and publication of results.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-9a1ebbbc-c032-4bdc-8940-8cc0ce126540