Abstract
References
- 1. Gerald WL, Miller HK, Battifora H, Miettinen M, Silva EG, Rosai J. Intra-abdominal desmoplastic small round-cell tumor. Report of 19 cases of a distinctive type of high-grade polyphenotypic malignancy affecting young individuals. The American journal of surgical pathology. 1991;15(6):499-513.
- 2. Ordóñez NG. Desmoplastic small round cell tumor: II: an ultrastructural and immunohistochemical study with emphasis on new immunohistochemical markers. The American journal of surgical pathology. 1998;22(11):1314-27.
- 3. Bildirici K, Tel N, İhtiyar E, Algin C. Desmoplastic small round cell tumor. Journal of Cumhuriyet University Faculty of Medicine. 2002;24:87-90.
- 4. Amato RJ, Ellerhorst JA, Ayala AG. Intraabdominal desmoplastic small cell tumor: Report and discussion of five cases. Cancer: Interdisciplinary International Journal of the American Cancer Society. 1996;78(4):845-51.
- 5. Sequencing HG. Finishing the euchromatic sequence of the human genome. Nature. 2004;431:931-45.
- 6. Wold S, Esbensen K, Geladi P. Principal component analysis. Chemometrics and intelligent laboratory systems. 1987;2(1-3):37-52.
- 7. Kocyigit Y, Korurek M. Classification of EMG signals using wavelet transform and fuzzy logic classifier. ITU Journal Series D: Engineering. 2005;4(3):25-31.
- 8. Blanchet FG, Legendre P, Borcard D. Forward selection of explanatory variables. Ecology. 2008;89(9):2623-32.
- 9. Han J, Kamber M, Pei J. Data mining concepts and techniques third edition. The Morgan Kaufmann Series in Data Management Systems. 2011;5(4):83-124.
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- 13. Guo Q, Wu W, Massart D, Boucon C, De Jong S. Feature selection in principal component analysis of analytical data. Chemometrics and Intelligent Laboratory Systems. 2002;61(1-2):123-32.
- 14. Bursa N, Tatlidil H. Evaluation of Independent Components Analysis from Statistical Perspective and Its Comparison with Principal Components Analysis. Journal of Suleyman Demirel University Institute of Science and Technology. 2020;24(2):474-86
- 15. Guo G, Wang H, Bell D, Bi Y, Greer K, editors. KNN model-based approach in classification. OTM Confederated International Conferences" On the Move to Meaningful Internet Systems"; 2003: Springer.
- 16. Kutluk T. Epidemiology of childhood cancers. IU Cerrahpasa Faculty of Medicine Continuing Medical Education Activities, Diagnosis of Pediatric Cancers for All Symposium Series. 2006(49):11-5.
- 17. Picarsic J, Reyes-Múgica M. Phenotype and immunophenotype of the most common pediatric tumors. Applied immunohistochemistry & molecular morphology. 2015;23(5):313-26.
- 18. Dean A, Byrne A, Marinova M, Hayden I. Clinical outcomes of patients with rare and heavily pretreated solid tumors treated according to the results of tumor molecular profiling. BioMed research international. 2016;2016.
- 19. Tosun Yildirim H, Yildirim A, Diniz Unlu AG, Aktas S, Vergin C. Childhood Malignant Solid Soft Tissue Tumors; Diagnostic, Histopathological And Molecular Approach. Journal of Izmir Dr. Behçet Uz Children's Hospital. 2019;9(1):1-9.
- 20. Dufresne A, Cassier P, Couraud L, Marec-Bérard P, Meeus P, Alberti L, et al. Desmoplastic small round cell tumor: current management and recent findings. Sarcoma. 2012;2012.
- 21. Li G, Li J, Ju Z, Sun Y, Kong J. A novel feature extraction method for machine learning based on surface electromyography from healthy brain. Neural Computing and Applications. 2019;31(12):9013-22.
- 22. Nirmalakumari K, Rajaguru H, Rajkumar P, editors. Pca and dwt based gene selection technique for classification of microarray data. 2018 3rd International Conference on Communication and Electronics Systems (ICCES); 2018: IEEE.
The Effects of Variable Selection and Dimension Reduction Methods on the Classification Model in the Small Round Blue Cell Tumor Dataset
Abstract
Objective: The purpose of this study is to investigate and compare the effects of different dimension reduction methods (PCA, ICA, PCA + Forward Selection, ICA + Forward Selection) on the K-NN classifier using open access gene expression data of small round blue cell tumor types.
Methods: In this study, open access gene expression data of small round blue cell tumor types was used for investigate and compare the effects of different dimension reduction methods. In the study, PCA, ICA, PCA + Forward Selection, ICA + Forward Selection were used as different dimension reduction methods together with K-NN classification method.
Results: Accuracy values obtained from the dimension reduction model made with PCA on K-NN model; for EWS, BL, NB, and RMS type tumors with 93.51%, 91.14%, 92.31%, and 94.74% respectively. Accuracy values obtained from the dimension reduction model made with PCA + Forward Selection on K-NN model; for EWS, BL, NB, and RMS type tumors with 96.25%, 96.25%, 95.06% and 95.47%, respectively. Accuracy values obtained from the dimension reduction model made with ICA on K-NN model; for EWS, BL, NB, and RMS type tumors with 91.89%, 90.67%, 88.31% and 89.47% respectively. Accuracy values obtained from the dimension reduction model made with ICA+ Forward Selection on K-NN model; for EWS, BL, NB, and RMS type tumors with 93.51%, 91.14%, 92.31% and 94.74% respectively.
Conclusion: In this study, the model created with PCA gives higher results than the model created with ICA. In addition, according to the results of the models obtained by applying the Forward selection method on these 2 models, the forward selection method has increased the classification performance.
Keywords
Dimension reduction principal component analysis independent component analysis K-NN Small round blue cell tumor.
References
- 1. Gerald WL, Miller HK, Battifora H, Miettinen M, Silva EG, Rosai J. Intra-abdominal desmoplastic small round-cell tumor. Report of 19 cases of a distinctive type of high-grade polyphenotypic malignancy affecting young individuals. The American journal of surgical pathology. 1991;15(6):499-513.
- 2. Ordóñez NG. Desmoplastic small round cell tumor: II: an ultrastructural and immunohistochemical study with emphasis on new immunohistochemical markers. The American journal of surgical pathology. 1998;22(11):1314-27.
- 3. Bildirici K, Tel N, İhtiyar E, Algin C. Desmoplastic small round cell tumor. Journal of Cumhuriyet University Faculty of Medicine. 2002;24:87-90.
- 4. Amato RJ, Ellerhorst JA, Ayala AG. Intraabdominal desmoplastic small cell tumor: Report and discussion of five cases. Cancer: Interdisciplinary International Journal of the American Cancer Society. 1996;78(4):845-51.
- 5. Sequencing HG. Finishing the euchromatic sequence of the human genome. Nature. 2004;431:931-45.
- 6. Wold S, Esbensen K, Geladi P. Principal component analysis. Chemometrics and intelligent laboratory systems. 1987;2(1-3):37-52.
- 7. Kocyigit Y, Korurek M. Classification of EMG signals using wavelet transform and fuzzy logic classifier. ITU Journal Series D: Engineering. 2005;4(3):25-31.
- 8. Blanchet FG, Legendre P, Borcard D. Forward selection of explanatory variables. Ecology. 2008;89(9):2623-32.
- 9. Han J, Kamber M, Pei J. Data mining concepts and techniques third edition. The Morgan Kaufmann Series in Data Management Systems. 2011;5(4):83-124.
- 10. Khan J, Wei JS, Ringner M, Saal LH, Ladanyi M, Westermann F, et al. Classification and diagnostic prediction of cancers using gene expression profiling and artificial neural networks. Nature medicine. 2001;7(6):673-9.
- 11. Remeseiro B, Bolon-Canedo V. A review of feature selection methods in medical applications. Computers in biology and medicine. 2019;112:103375.
- 12. Song F, Guo Z, Mei D, editors. Feature selection using principal component analysis. 2010 international conference on system science, engineering design and manufacturing informatization; 2010: IEEE.
- 13. Guo Q, Wu W, Massart D, Boucon C, De Jong S. Feature selection in principal component analysis of analytical data. Chemometrics and Intelligent Laboratory Systems. 2002;61(1-2):123-32.
- 14. Bursa N, Tatlidil H. Evaluation of Independent Components Analysis from Statistical Perspective and Its Comparison with Principal Components Analysis. Journal of Suleyman Demirel University Institute of Science and Technology. 2020;24(2):474-86
- 15. Guo G, Wang H, Bell D, Bi Y, Greer K, editors. KNN model-based approach in classification. OTM Confederated International Conferences" On the Move to Meaningful Internet Systems"; 2003: Springer.
- 16. Kutluk T. Epidemiology of childhood cancers. IU Cerrahpasa Faculty of Medicine Continuing Medical Education Activities, Diagnosis of Pediatric Cancers for All Symposium Series. 2006(49):11-5.
- 17. Picarsic J, Reyes-Múgica M. Phenotype and immunophenotype of the most common pediatric tumors. Applied immunohistochemistry & molecular morphology. 2015;23(5):313-26.
- 18. Dean A, Byrne A, Marinova M, Hayden I. Clinical outcomes of patients with rare and heavily pretreated solid tumors treated according to the results of tumor molecular profiling. BioMed research international. 2016;2016.
- 19. Tosun Yildirim H, Yildirim A, Diniz Unlu AG, Aktas S, Vergin C. Childhood Malignant Solid Soft Tissue Tumors; Diagnostic, Histopathological And Molecular Approach. Journal of Izmir Dr. Behçet Uz Children's Hospital. 2019;9(1):1-9.
- 20. Dufresne A, Cassier P, Couraud L, Marec-Bérard P, Meeus P, Alberti L, et al. Desmoplastic small round cell tumor: current management and recent findings. Sarcoma. 2012;2012.
- 21. Li G, Li J, Ju Z, Sun Y, Kong J. A novel feature extraction method for machine learning based on surface electromyography from healthy brain. Neural Computing and Applications. 2019;31(12):9013-22.
- 22. Nirmalakumari K, Rajaguru H, Rajkumar P, editors. Pca and dwt based gene selection technique for classification of microarray data. 2018 3rd International Conference on Communication and Electronics Systems (ICCES); 2018: IEEE.
Details
| Primary Language | English |
|---|---|
| Subjects | Health Care Administration |
| Journal Section | Research articles |
| Authors | |
| Publication Date | December 31, 2021 |
| Published in Issue | Year 2021 Volume: 7 Issue: 3 |
e-ISSN 2149-7796