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Year 2022, Volume 5, Issue 1, 84 - 89, 30.04.2022
https://doi.org/10.35377/saucis...1094043

Abstract

References

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  • [15] M. Song, H. Jung, S. Lee, D. Kim, and M. Ahn, “Diagnostic Classification and Biomarker Identification of Alzheimer’s Disease with Random Forest Algorithm,” Brain Sciences, vol. 11, no. 4, pp. 453, 2021.
  • [16] A. W. Warr, “Scientific workflow systems: Pipeline Pilot and KNIME,” J Comput Aided Mol Des., vol. 26, no.4, pp. 801–804, 2012.
  • [17] Y. Yuan and R. Little, “Meta-Analysis of Studies with Missing Data,” Biometrics, vol.65, pp. 487-496, 2009.
  • [18] J. M. Schauer, K. Diaz, T.D. Pigott and J. Lee, “Exploratory Analyses for Missing Data in Meta-Analyses and Meta-Regression: A Tutorial,” Alcohol and Alcoholism, vol. 57, pp. 35-36, 2022.

Comparision of Different Machine Learning Algorithms to Predict the Diagnostic Accuracy Parameters of Celiac Serological Tests

Year 2022, Volume 5, Issue 1, 84 - 89, 30.04.2022
https://doi.org/10.35377/saucis...1094043

Abstract

Celiac disease; is an autoimmune digestive system disease characterized by chronic intestinal inflammation and villus antrophy and triggered by dietary gluten genetically susceptible individuals. Diagnosis is based on serological tests and small bowel biopsy. Because of the diversity in the clinical features of the disease, various patient profile and the non-standardized serological tests, it is difficult to diagnose the celiac disease. Sensitivity, specificity, positive and negative predictive values are important parameters for the accuracy of the tests and they are missing in some clinicial studies. It is difficult do standardize the tests with these missing values for clinicians. The aim of this study is to train different machine learning algorithms and to test their performance in prediction of the diagnostic accurary parameters of celiac serological tests. Decision trees are effective machine learning algorithms for predicting potential covariates with %88,7 accuracy.

References

  • [1] D. Schuppan, “Current concepts of celiac disease pathogenesis,” Gastroenterology, vol. 119, pp. 234–242, 2000.
  • [2] S. Lohi et al, “Increasing prevalence of coeliac disease over time,” Alimentary Pharmacology &Therapeutics, vol. 26, no. 9, pp. 1217-25, 2005.
  • [3] M. Parizade, Y. Bujanover, B. Weiss V., Nachmias and B. Shainberg, “Performance of Serology Assays for Diagnosing Celiac Disease in a Clinical Setting,” Clinical and Vaccine Immunology, vol. 16, pp. 1576–1582, 2009.
  • [4] A. Fasano and C. Catassi, “Current approaches to diagnosis and treatment of celiac disease: An evolving spectrum”, Gastroenterology, vol. 120, no. 3, pp. 636-51, 2001.
  • [5] A. Marlou and A.D. Leffler, “Serum Markers in the Clinical Management of Celiac Disease,” Digestive Disease, vol. 33, pp. 236–243, 2015.
  • [6] D. Basso et al. “A new indirect chemiluminescent immunoassay to measure Anti-Tissue Transglutaminase antibodies,” J Pediatr Gastroenterol Nutr., vol. 43, pp. 613-8, 2006.
  • [7] P. Eusebi, “Diagnostic Accuracy Measures,” Cerebrovascular Diseases, vol.36, pp. 267–272, 2013.
  • [8] A. Hoyer and A. Zapf, “Studies for the Evaluation of Diagnostic Tests,” Deutsches Ärzteblatt International, vol. 18, pp. 555–60, 2021.
  • [9] O. Rozenberg, A. Lerner, A. Pacht, M. Grinberg, D. Reginashvili, C. Henig and M. Barak, “A new algorithm for the diagnosis of celiac disease,” Cellular & Molecular Immunology, vol. 8, pp. 146–149, 2011.
  • [10] Z. Obermeyer and J. E. Emanuel, “Predicting the Future-Big Data, Machine Learning, and Clinical Medicine,” N Engl J Med, vol. 375, no.13, pp. 1216–1219, 2016.
  • [11] M. Saken, M. Y. Banzragch and N. Yumusak, “Impact of image segmentation techniques on celiac disease classification using scale invariant texture descriptors for standard flexible endoscopic systems,” Turkish Journal of Electrical Engineering & Computer Sciences, vol. 29, pp. 598 – 615, 2021.
  • [12] R. Bellazzi and B. Zupan, “Predictive data mining in clinical medicine: Current issues and guidelines,” International Journal of Medical Informatics, vol.77, pp.81–97, 2008.
  • [13] Y. Long, L. Wang and M. Sun, “Structure Extension of Tree-Augmented Naive Bayes,” Entropy, vol. 21, pp.721, 2019.
  • [14] Z. Zhang, Y. Zhao, A. Canes, D. Steinberg and O. Lyashevska, “Predictive analytics with gradient boosting in clinical medicine,” Annals of Translational Medicine, vol. 7, no.7, pp.152, 2019.
  • [15] M. Song, H. Jung, S. Lee, D. Kim, and M. Ahn, “Diagnostic Classification and Biomarker Identification of Alzheimer’s Disease with Random Forest Algorithm,” Brain Sciences, vol. 11, no. 4, pp. 453, 2021.
  • [16] A. W. Warr, “Scientific workflow systems: Pipeline Pilot and KNIME,” J Comput Aided Mol Des., vol. 26, no.4, pp. 801–804, 2012.
  • [17] Y. Yuan and R. Little, “Meta-Analysis of Studies with Missing Data,” Biometrics, vol.65, pp. 487-496, 2009.
  • [18] J. M. Schauer, K. Diaz, T.D. Pigott and J. Lee, “Exploratory Analyses for Missing Data in Meta-Analyses and Meta-Regression: A Tutorial,” Alcohol and Alcoholism, vol. 57, pp. 35-36, 2022.

Details

Primary Language English
Subjects Computer Science, Interdisciplinary Application
Journal Section Articles
Authors

Özgül ÖZER (Primary Author)
İSTANBUL ÜNİVERSİTESİ, FEN BİLİMLERİ ENSTİTÜSÜ
0000-0003-4265-3563
Türkiye


Nazlı ARDA
ISTANBUL UNIVERSITY, FACULTY OF SCIENCE
0000-0002-1043-5652
Türkiye

Publication Date April 30, 2022
Application Date March 27, 2022
Acceptance Date April 4, 2022
Published in Issue Year 2022, Volume 5, Issue 1

Cite

IEEE Ö. Özer and N. Arda , "Comparision of Different Machine Learning Algorithms to Predict the Diagnostic Accuracy Parameters of Celiac Serological Tests", Sakarya University Journal of Computer and Information Sciences, vol. 5, no. 1, pp. 84-89, Apr. 2022, doi:10.35377/saucis...1094043