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- Title
Miyeloproliferatif Hastalık Ön Tanılı Hastalarda Makine Öğrenmesi Yöntemi ile Genetik Test Seçimine İlişkin Metodolojik Bir Modelleme Çalışması.
- Authors
KUBAT, Gözde; ŞAHİN, Feride İffet; ÇELİK, Bülent
- Abstract
Objective: In order to diagnose myeloproliferative diseases, the presence of genetic mutations is examined by the clinician in line with the disease diagnosis scheme determined by the World Health Organization. In this study, it is aimed to predict the appropriate diagnostic screening test with the model created based on bone marrow and complete blood count findings of patients who applied to the clinic. Accordingly, it is aimed to save time and financial for patients who apply to the clinic. Material and Methods: Prediction was made using the machine learning algorithms by considering the findings of patients referred to the Başkent University Ankara Hospital, Department of Medical Genetics Genetic Diseases Diagnosis Center. Descriptive statistics of the study data were given as median, and statistically significant differences were investigated using the Kruskal-Wallis test statistic. Classification algorithms such as Naive Bayes, K-Nearest Neighbor, Linear Discriminant Analysis, Support Vector Machines, Entropy Based Classification and Decision Tree were used in the estimation. With the algorithms, determinative values such as accuracy, specificity and sensitivity were obtained. The estimations made were examined according to the accuracy rates obtained and the best model was tried to be selected. Results: Statistically significant differences were found between the examined complete blood count values and the presence of mutation. The accuracy rates were found to be around 60% in the models created with Naive Bayes, K-Nearest Neighbor, Linear Discriminant Analysis, Support Vector Machines, Entropy Based Classification and Decision Tree Algorithms. Conclusion: Although the accuracy rate obtained from the machine learning algorithms used is at a moderate level, it was concluded that the results of the study would make a significant contribution to the field, since similar studies have not been included in the literature.
- Subjects
FISHER discriminant analysis; MEDICAL genetics; SUPPORT vector machines; MACHINE learning; WORLD Health Organization; K-nearest neighbor classification; NAIVE Bayes classification; DECISION trees; BLOOD cell count
- Publication
Turkiye Klinikleri Journal of Biostatistics, 2022, Vol 14, Issue 1, p45
- ISSN
1308-7894
- Publication type
Article
- DOI
10.5336/biostatic.2021-86989