Determination of Factors Affecting Severity of Helicobacter pylori for Gastric Biopsy Samples by CART Decision Tree Algorithm

Year 2023, Volume: 9 Issue: 3, 429 - 439, 31.08.2023

Türkan Mutlu Yar Ülkü Karaman Yeliz Kaşko Arıcı

https://doi.org/10.19127/mbsjohs.1316728

Abstract

Objective: H. pylori wich is one of the important gastric pathogens and is a motile, non-sporeless, encapsulated, microaerophilic, gram-negative bacterium. The aim of this study was to determine the factors affecting disease severity in patients with a positive pathologic diagnosis of Helicobacter pylori after gastric biopsy by data mining. It was aimed to utilize the more descriptive structure of data mining algorithms compared to traditional classification and regression approaches.
Methods: The study data were obtained from gastric biopsy samples of 1247 patients, 40.5% male and 59.5% female, who were sent to the pathology laboratory between 2014 and 2018. A total of 6 factors including age, gender, inflammation, metaplasia, atrophy and activation, which are thought to have an effect on gastric H. pylori severity, were examined. Querying the effects of factors was done with the CART (Classification and Regression Trees) decision tree algorithm, one of the data mining algorithms.
Results: The factors ranking as their effect on the severity of gastric h. pylori, as follows; activation > inflammation > metaplasia > atrophy > age > gender in a percentage of normalized importance at 100.00%, 88.6%, 51.4%, 38.1%, 12.8%, 3.3% respectively.
Conclusion: As a result, levels of activation, inflammation, and metaplasia emerged as the most important factors affecting gastric H. pylori severity.

Keywords

Data mining, Decision Tree, CART Algorithm, H. pylori stomach

Supporting Institution

-

Project Number

-

Thanks

-

References

• Asaka M, Sugiyama T, Nobuta A, Kato M, Takeda H, Graham DY. Atrophic gastritis and intestinal metaplasia in Japan: Results of a largemulticenterstudy. Helicobacter, 2001;6, 294-9.
• Breiman L, Friedman JH, Olshen R, & Stone, ACG. (1984). Classification and Regression Trees. Wadsworth International Group, Belmont, California, USA.
• Chang LY & Wang HW. Analysis of Traffic Injury Severity: An Application of NonParametric Classification Tree Techniques. Accident Analysis & Prevention, 2006;38, 1019-1027.
• Dağdartan U. (2011). Isolation of Helicobacter pylori from Gastric and Duodenum Biopsy Samples and Investigation of Antimicrobial Resistance. Department of Medical Microbiology, Specialization Thesis in Medicine. Myrtle
• Erdem B: Campylobacterand Helicobacter. Basic and Clinical Microbiology. UstacelebiSh. Güneş Bookstore, Ankara, 1999; pp. 531–40
• Greff K, Srivastava RK, Koutník J, Steunebrink BR. and SchmidhuberJ. LSTM: a search space odyssey. IEEE Transactions Neural Network Learning System, 2016;28(10): 2222–2232
• Güner A, Telli N Helicobacter pylori: A New Food Pathogen? Erciyes University Journal of Vet Fak 2012;9(1) 51-63.
• Hirschl AM, Makristathis A. MethodstodetectHelicobacterpylorifromculturetomolecularbiology. Helicobacter 2007; 12(Suppl 2): 6-11.
• Kadanalı A, Özkurt Z. Helicobacter pylori infection: Epidemiology, pathogenesis and related diseases. KlimikJournal 2004; 17(3): 146-50.
• Cutter U, Comparison of intestinal metaplasiaandHelicobacterpylori scores in patients undergoing upper gastrointestinal endoscopy. CukurovaMed J, 2018;43(3): 574-580.
• Karaman Ü, (2020). Evaluation of the diagnosis of Helicobacter pylori from gastric biopsy samples by staining methods. Giresun University Medical Microbiology Department, Master's thesis. Giresun.
• Kumar SA & Vijayalakshmi MN. (2011). Efficiency of decisiontrees in predictingstudent’sacademicperformance. First International Conference on Computer Science, Engineering and Applications, India.
• Kurtuluş A, Akın M, Buldukoğlu OÇ, Yalçınkaya T, Yıldırım B, Gelen MT. The Frequency of Helicobacter pylori and the Demographic, EndoscopicandHistopathologicalCharacteristics of the Patients Performed Endoscopy in the Tertiary Health Institution in Antalya RegionAkd Tip D, 2017;2:101-106.
• Kusters JG, vanVliet AHM, Kuipers EJ. Pathogenesis of Helicobacterpylori infection. Clin Microbiol Rev 2006; 19(3): 449-90.
• Mirza E. (2001). The prevalence of Helicobacter pylori in patients with metabolic syndrome. Gazi UniversityFaculty of Medicine, Department of Internal Medicine. Master thesis. Ankara.
• Rutkowski, L., Jaworski, M., Pietruczuk, L., &Duda P. The CART decision tree for mining data streams. Information Sciences, 2014;266,1-15. https://doi.org/10.1016/j.ins.2013.12.060
• Priyama A, Abhijeeta RG, Ratheeb A, Srivastavab S. Comparative analysis of decision tree classification algorithms. International Journal of Current Engineering and Technology. 2013;3(2):334-7.
• Sasa G, Milosav S, Vuka K. (2002). The relationship between the density of Helicobacter pylori colonisation and the degree of gastritis severity. Gastro enterol hepatol, 21, 3- 4.
• Sipponen P, Stolte M. (1997). Clinical impact of routine biopsies of the gastric ant rumand body. Endoscopy, 29, 671-8.
• Topal D, Göral V, Yılmaz F. Association of Helicobacter pylori with Intestinal Metaplasia, Gastric Atrophy and Bcl-2. Turkey Clinics J Gastroentero hepatol, 2004;15, 65-73.
• Tuncel F, BozkurtF, Gülseren A, Usta Y. The relationship between the incidence of celiac disease and Helicobacter pylori gastritis in childhood. Endoscopy. 2019;27(1):16-19.
• Uyanık MH, Aktaş O. Microbiological diagnosis of Helicobacter pylori. EAJM 2007;39(3): 205-9.