<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>This paper presents a method that binds statistical and data mining techniques, which aims to support the decision-making process in selected diseases of the digestive system. 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 hospitalised in the Department of Gastroenterology and Internal Diseases, Bialystok, and finding the symptoms differentiating the two analysed diseases. The second goal is to build a system that clearly points to one of the two diseases UC or CD, which shortens the time of diagnosis and facilitates the future treatment of patients. The work focuses on building a model that can be the basis for the construction of action rules, which are one of the basic elements in the medical recommendation system. Generated action rules indicated differentiating factors, such as mean corpuscular volume, platelets (PLTs), neutrophils, monocytes, eosinophils, basophils, alanine aminotransferase (ALAT), creatinine, sodium and potassium. Other important parameters were smoking and blood in stool.</p>
</abstract>

This paper presents a method that binds statistical and data mining techniques, which aims to support the decision-making process in selected diseases of the digestive system. 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 hospitalised in the Department of Gastroenterology and Internal Diseases, Bialystok, and finding the symptoms differentiating the two analysed diseases. The second goal is to build a system that clearly points to one of the two diseases UC or CD, which shortens the time of diagnosis and facilitates the future treatment of patients. The work focuses on building a model that can be the basis for the construction of action rules, which are one of the basic elements in the medical recommendation system. Generated action rules indicated differentiating factors, such as mean corpuscular volume, platelets (PLTs), neutrophils, monocytes, eosinophils, basophils, alanine aminotransferase (ALAT), creatinine, sodium and potassium. Other important parameters were smoking and blood in stool.

Selected Morphotic Parameters Differentiating Ulcerative Colitis from Crohn’s Disease

Acta Mechanica et Automatica

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

This paper presents a method that binds statistical and data mining techniques, which aims to support the decision-making process in selected diseases of the...

Selected Morphotic Parameters Differentiating Ulcerative Colitis from Crohn’s Disease

Publié en ligne: 29 nov. 2021

Pages: 249 - 253

Reçu: 22 mai 2020

Accepté: 24 sept. 2021

DOI: https://doi.org/10.2478/ama-2021-0031

Mots clés
action rule, data mining, colon disease, morphotic parameters, smoking

© 2021 Anna Kasperczuk, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Selected Morphotic Parameters Differentiating Ulcerative Colitis from Crohn’s Disease

Publié en ligne: 29 nov. 2021

Pages: 249 - 253

Reçu: 22 mai 2020

Accepté: 24 sept. 2021

DOI: https://doi.org/10.2478/ama-2021-0031

Mots clésaction rule, data mining, colon disease, morphotic parameters, smoking

© 2021 Anna Kasperczuk, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Mots clés
action rule, data mining, colon disease, morphotic parameters, smoking