Logistic regression as a relevant statistical tool for medical data investigation and evaluation

• Autorzy: Kuráňová P.
• Języki publikacji: EN

Abstrakty

EN

This paper presents the usage of logistic regression for predicting the classification of patients into one of the two groups. Our data come from patients who underwent Phadiatop test examinations and patients who underwent colectomy in the University Hospital of Ostrava. As the predictor variables were chosen personal and family anamneses for Phadiatop test and the physiological and operative scores for colectomy. For Phadiatop test, both of these anamneses were divided into four categories according to severity ranked by doctors. Scores for morbidity were based on the POSSUM system. The psychological score comprises 12 factors and the operative score comprises 6. The categorical dependent variable which we want to predict was Phadiatop test (respectively morbidity). The model for Phadiatop test was tested with the use of a medical database of 1027 clients and morbidity was tested upon a medical database of 364 clients. The developed models predict the right results with 75% probability for Phadiatop test and 70% probability for morbidity in surgery.

Słowa kluczowe

EN

logistic regression; medical data; atopy; Phadiatop test; surgery; morbidity

• Wydawca: Polskie Towarzystwo Bezpieczeństwa i Niezawodności
• Czasopismo: Journal of Polish Safety and Reliability Association
• Rocznik: 2011
• Tom: Vol. 2, No. 1
• Strony: 137--142
• Opis fizyczny: Bibliogr. 7 poz., tab.

Twórcy

autor

Kuráňová P.

• VŠB – Technical University of Ostrava, Ostrava Poruba, Czech Republic

Bibliografia

• [1] Hajduková, Z., Pólová, J. & Kosek, V. (2005). The importance of Atopy Investigation in the Department of Travel Medicine. Allergies: Magazine for continuous education in allergy and clinical immunology, No.2, 106-109.
• [2] Hajduková, Z., Vantuchová, Y., Klimková, P., Makhoul, M. & Hromádka, R. (2009). Atopy in patiens with allergic contact dermatitis. Jurnal of Czech Physicians: Occupational therapy, No.2, 69-73.
• [3] Hosmer, D.W. & Lemeshow, S. (2000). Applied Logistic Regression. NewYork: WileyInterscience
• [4] Kuráňová, P., Praks, P. & Hajduková, Z. (2010). Logistic regression as a tool for athopy investigation. Mendel 2010, 187-190.
• [5] Kuráňová, P., Praks, P. & Hajduková, Z. (2010). Statitical modelling of the Phadiatop test. WOFEX 2010. 174-178.
• [6] Martínek, L. (2006). Aplikace specializovaných skórovacích systémů pro objektivizaci rizik laparoskopických operací kolorekta. The doctoral thesis.
• [7] Rabasová, M., Briš, R. & Kuráňová, P. (2010). Modified logistic regression as a tool for discrimination. Reliability, Risk, and Safety: Theory and Applications. R. Briš, C. Guedes Soares, S. Martorell (eds.); Vol 3, 1967-1972.