Effect of data preprocessing on ensemble learning for classification in disease diagnosis

References

• Acock, A. C. 2005. Working with missing values. Journal of Marriage and Family 67 (4):1012–28.
   Web of Science ®Google Scholar
• Acuña, E, and C. Rodriguez. 2004. The treatment of missing values and its effect on classifier accuracy. In Classification, clustering, and data mining applications. studies in classification, data analysis, and knowledge organisation, ed. D. Banks, F. R. McMorris, P. Arabie, W. Gaul. Berlin, Heidelberg: Springer. doi:10.1007/978-3-642-17103-1_60.
   Google Scholar
• Alanis-Tamez, M. D., Y. Villuendas-Rey, and C. Yáñez-Márquez. 2017. Computational intelligence algorithms applied to the pre-diagnosis of chronic diseases. Research in Computing Science 138 (1):41–50. Accessed July 6, 2019. https://www.semanticscholar.org/paper/Computational-Intelligence-Algorithms-Applied-to-of-Alanis-Tamez-Villuendas-Rey/055677db623b94f2cc940b94e3c732c111a9677d. doi:10.13053/rcs-138-1-4.
   Google Scholar
• Alcalá-Fdez, J., L. Sánchez, S. García, M. J. del Jesus, S. Ventura, J. M. Garrell, J. Otero, C. Romero, J. Bacardit, V. M. Rivas, et al. 2009. KEEL: A software tool to assess evolutionary algorithms for data mining problems. Soft Computing 13 (3):307–18. doi:10.1007/s00500-008-0323-y.
   Web of Science ®Google Scholar
• Alghamdi, M., M. Al-Mallah, S. Keteyian, C. Brawner, J. Ehrman, and S. Sakr. 2017. Predicting diabetes mellitus using SMOTE and ensemble machine learning approach: The Henry Ford ExercIse Testing (FIT) project. PLoS One 12 (7):e0179805. doi:10.1371/journal.pone.0179805.
   PubMed Web of Science ®Google Scholar
• Allison, P. 2002. Missing data. 1st ed. San Francisco, CA, USA: SAGE Publications, Inc. doi:10.4135/9781412985079.
   Google Scholar
• Amaratunga, D., J. Cabrera, and Y. S. Lee. 2008. Enriched random forests. Bioinformatics 24 (18):2010–4. doi:10.1093/bioinformatics/btn356.
   PubMed Web of Science ®Google Scholar
• Azencott, C. 2018. Machine learning and genomics: Precision medicine vs. patient privacy. Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences 376(20170350):1–13. doi:10.1098/rsta.2017.0350
   Google Scholar
• Bennet, D. A. 2001. How can I deal with missing data in my study? Australian and New Zealand Journal of Public Health 25 (5):464–9.
   PubMed Web of Science ®Google Scholar
• Bradley, A. P. 1997. The use of the area under the ROC curve in the evaluation of machine learning algorithms. Pattern Recognition 30 (7):1145–59. doi:10.1016/S0031-3203(96)00142-2.
   Web of Science ®Google Scholar
• Breiman, L. 2001. Random forests. Machine Learning 45 (1):5–32. doi:10.1023/A:1010933404324.
   Web of Science ®Google Scholar
• Brodley, C. E, and M. A. Friedl. 1999. Identifying mislabeled training data. Journal of Artificial Intelligence Research 11:131–67. doi:10.1613/jair.606.
   Web of Science ®Google Scholar
• Buckland, M, and F. Gey. 1994. The relationship between Recall and Precision. Journal of the American Society for Information Science 45 (1):12–9. doi:10.1002/(SICI)1097-4571(199401)45:1<12::AID-ASI2>3.0.CO;2-L.
   Web of Science ®Google Scholar
• Chawla, N. V., K. W. Bowyer, L. O. Hall, and W. P. Kegelmeyer. 2002. SMOTE: Synthetic minority over-sampling technique. Journal of Artificial Intelligence Research 16:321–57. doi:10.1613/jair.953.
   Web of Science ®Google Scholar
• Chen, J. H, and S. M. Asch. 2017. Machine learning and prediction in medicine – Beyond the peak of inflated expectations. The New England Journal of Medicine 376 (26):2507–9. doi:10.1056/NEJMp1702071.
   PubMed Web of Science ®Google Scholar
• Cohen, J. 1960. A coefficient of agreement for nominal scales. Educational and Psychological Measurement 20 (1):37–46. doi:10.1177/001316446002000104.
   Web of Science ®Google Scholar
• Dietterich, T. G. 2000. Ensemble methods in machine learning. In International Workshop on Multiple Classifier Systems, Springer, Berlin, 1–15. doi:10.1007/3-540-45014-9_1.
   Google Scholar
• Dong, Y, and C. Y. J. Peng. 2013. Principled missing data methods for researchers. SpringerPlus 2 (1):222. doi:10.1186/2193-1801-2-222.
   PubMed Web of Science ®Google Scholar
• Fatima, M, and M. Pasha. 2017. Survey of machine learning algorithms for disease diagnostic. Journal of Intelligent Learning Systems and Applications 9 (1):1–16. doi:10.4236/jilsa.2017.91001.
   Google Scholar
• Fernández, A., V. López, M. Galar, M. J. del Jesus, and F. Herrera. 2013. Analysing the classification of imbalanced data-sets with multiple classes: Binarization techniques and ad-hoc approaches. Knowledge-Based Systems 42:97–110. doi:10.1016/j.knosys.2013.01.018.
   Web of Science ®Google Scholar
• Fluss, R., D. Faraggi, and B. Reiser. 2005. Estimation of the Youden index and its associated cutoff point. Biometrical Journal. Biometrische Zeitschrift 47 (4):458–72. doi:10.1002/bimj.200410135.
   PubMed Web of Science ®Google Scholar
• Folorunso, S. O, and A. B. Adeyemo. 2013. Alleviating classification problem of imbalanced dataset. African Journal of Computing & ICT 6 (2):137–44.
   Google Scholar
• Frénay, B, and M. Verleysen. 2014. Classification in the presence of label noise: A survey. IEEE Transactions on Neural Networks and Learning Systems 25 (5):845–69. doi:10.1109/TNNLS.2013.2292894.
   PubMed Web of Science ®Google Scholar
• Friedman, J. H. 2001. Greedy function approximation: A gradient boosting machine. The Annals of Statistics 29 (5):1189–232. doi:10.1214/aos/1013203451.
   Web of Science ®Google Scholar
• Friedman, J., T. Hastie, and R. Tibshirani. 2000. Additive logistic regression: A statistical view of boosting. The Annals of Statistics 28 (2):337–407. doi:10.1214/aos/1016218223.
   Web of Science ®Google Scholar
• Gammerman, A. 2010. Modern machine learning techniques and their applications to medical diagnostics. Berlin: Springer. doi:10.1007/978-3-642-16239-8_2.
   Google Scholar
• Gunčar, G., M. Kukar, M. Notar, M. Brvar, P. Černelč, M. Notar, and M. Notar. 2018. An application of machine learning to haematological diagnosis. Scientific Reports 8 (411):1–12. doi:10.1038/s41598-017-18564-8.
   PubMedGoogle Scholar
• Hajian-Tilaki, K. 2013. Receiver operating characteristic (ROC) curve analysis for medical diagnostic test evaluation. Caspian Journal of Internal Medicine 4 (2):627–35.
   PubMedGoogle Scholar
• Ho, T. K. 1998. The random subspace method for constructing decision forests. IEEE Transactions on Pattern Analysis and Machine Intelligence 20 (8):832–44. doi:10.1109/34.709601.
   Web of Science ®Google Scholar
• Hu, S., Y. Liang, L. Ma, and Y. He. 2009. MSMOTE: Improving classification performance when training data is imbalanced. 2nd International Workshop on Computer Science and Engineering, WCSE 2009, Qingdao, China, vol. 2, 13–7. doi:10.1109/WCSE.2009.756.
   Google Scholar
• Jutel, A. 2011. Classification, disease, and diagnosis. Perspectives in Biology and Medicine 54 (2):189–205. doi:10.1353/pbm.2011.0015.
   PubMed Web of Science ®Google Scholar
• Kang, H. 2013. The prevention and handling of the missing data. Korean Journal of Anesthesiology 64 (5):402–6. doi:10.4097/kjae.2013.64.5.402.
   PubMedGoogle Scholar
• Khalilia, M., S. Chakraborty, and M. Popescu. 2011. Predicting disease risks from highly imbalanced data using random forest. BMC Medical Informatics and Decision Making 11 (1):51. doi:10.1186/1472-6947-11-51.
   PubMed Web of Science ®Google Scholar
• Kononenko, I. 2001. Machine learning for medical diagnosis: History, state of the art and perspective. Artificial Intelligence in Medicine 23 (1):89–109. doi:10.1016/S0933-3657(01)00077-X.
   PubMed Web of Science ®Google Scholar
• Kotsiantis, S. 2011. Combining bagging, boosting, rotation forest and random subspace methods. Artificial Intelligence Review 35 (3):223–40. doi:10.1007/s10462-010-9192-8.
   Web of Science ®Google Scholar
• Krawczyk, B, and B. T. McInnes. 2018. Local ensemble learning from imbalanced and noisy data for word sense disambiguation. Pattern Recognition 78:103–19. doi:10.1016/j.patcog.2017.10.028.
   Web of Science ®Google Scholar
• Kübler, S., C. Liu, and Z. A. Sayyed. 2017. To use or not to use: Feature selection for sentiment analysis of highly imbalanced data. Natural Language Engineering 24:3–37. doi:10.1017/S1351324917000298.
   Web of Science ®Google Scholar
• Kumar, G. R., V. S. Kongara, and G. A. Ramachandra. 2013. An efficient ensemble based classification techniques for medical diagnosis. International Journal of Latest Technology in Engineering, Management & Applied Science 2 (8):5–9. Accessed July 5, 2019. https://www.academia.edu/35248870/An_Efficient_Ensemble_Based_Classification_Techniques_for_Medical_Diagnosis.
   Google Scholar
• Lavanya, D, and U. Rani. 2012. Ensemble decision tree classifier for breast cancer data. International Journal of Information Technology Convergence and Services 2 (1):17–24. doi:10.5121/ijitcs.2012.2103.
   Google Scholar
• Little, R, and D. Rubin. 1987. Statistical analysis with missing data. 1st ed. New Jersey, USA: John Wiley & Sons, Inc. doi:10.4135/9781412985079.
   Google Scholar
• Little, R. J. A., and D. B. Rubin. 1991. Statistical Analysis with Missing Data. Journal of Educational Statistics, 16 (2):150–5.
   Google Scholar
• Liu, A. Y. C. 2004. The effect of oversampling and undersampling on classifying imbalanced text datasets (Doctoral dissertation, University of Texas at Austin).
   Google Scholar
• Maimon, O, and L. Rokach. 2005. Data mining and knowledge discovery handbook. Berlin: Springer-Verlag.
   Google Scholar
• McHugh, M. L. 2012. Interrater reliability: The kappa statistic. Biochemia Medica 22 (3):276–82. doi:10.11613/BM.2012.031.
   PubMed Web of Science ®Google Scholar
• Mislevy, R., R. Little, and D. Rubin. 1991. Statistical analysis with missing data. Journal of Educational Statistics 16 (2):150–5. doi:10.2307/1165119.
   Google Scholar
• Moon, H., H. Ahn, R. L. Kodell, S. Baek, C. J. Lin, and J. J. Chen. 2007. Ensemble methods for classification of patients for personalized medicine with high-dimensional data. Artificial Intelligence in Medicine 41 (3):197–207. doi:10.1016/J.ARTMED.2007.07.003.
   PubMed Web of Science ®Google Scholar
• Morales, P., J. Luengo, L. P. F. Garcia, A. C. Lorena, A. C. P. L. F. Carvalho, and F. Herrera. 2017. The NoiseFiltersR package: Label noise preprocessing in R. The R Journal 9 (1):219–28. doi:10.32614/RJ-2017-027.
   Google Scholar
• Obermeyer, Z, and E. J. Emanuel. 2016. Predicting the future – Big data, machine learning, and clinical medicine. The New England Journal of Medicine 375 (13):1216–9. doi:10.1056/NEJMp1606181.
   PubMed Web of Science ®Google Scholar
• Sáez, J. A., B. Krawczyk, and M. Woźniak. 2016. On the influence of class noise in medical data classification: Treatment using noise filtering methods. Applied Artificial Intelligence 30 (6):590–609. doi:10.1080/08839514.2016.1193719.
   Web of Science ®Google Scholar
• Salcedo-Bernal, A., M. P. Villamil-Giraldo, and A. D. Moreno-Barbosa. 2016. Clinical data analysis: An opportunity to compare machine learning methods. Procedia Computer Science 100:731–8. doi:10.1016/j.procs.2016.09.218.
   Google Scholar
• Salzberg, S. L. 1994. C4.5: Programs for machine learning. Machine Learning 16 (3):235–40. doi:10.1007/BF00993309.
   Google Scholar
• Schafer, J. L. 1999. Multiple imputation: A primer. Statistical Methods in Medical Research 8 (1):3–15. doi:10.1177/096228029900800102.
   PubMed Web of Science ®Google Scholar
• Shah, P., F. Kendall, S. Khozin, R. Goosen, J. Hu, J. Laramie, M. Ringel, and N. Schork. 2019. Artificial intelligence and machine learning in clinical development: A translational perspective. npj Digital Medicine 2 (1):1–5. doi:10.1038/s41746-019-0148-3.
   PubMedGoogle Scholar
• Sinharay, S., H. S. Stern, and D. Russell. 2001. The use of multiple imputation for the analysis of missing data. Psychological Methods 6 (4):317–29. doi:10.1037/1082-989X.6.4.317.
   PubMed Web of Science ®Google Scholar
• Sutton, C. D. 2005. Classification and regression trees, bagging, and boosting. In Handbook of statistics, ed. R. C. Rao and A. S. R. S. Rao, 24th ed., 303–30. San Francisco, CA, USA: Elsevier. doi:10.1016/S0169-7161(04)24011-1.
   Google Scholar
• Swets, J. 1988. Measuring the accuracy of diagnostic systems. Science 240 (4857):1285–93. doi:10.1126/science.3287615.
   PubMed Web of Science ®Google Scholar
• Thottakkara, P., T. Ozrazgat-Baslanti, B. Hupf, P. Rashidi, P. Pardalos, P. Momcilovic, and A. Bihorac. 2016. Application of machine learning techniques to high-dimensional clinical data to forecast postoperative complications. PLoS One 11 (5):e0155705. doi:10.1371/journal.pone.0155705.
   PubMed Web of Science ®Google Scholar
• UCI. 2019. UC Irvine Machine Learning Repository. Center for Machine Learning and Intelligent Systems. Accessed July 27, 2019. https://archive.ics.uci.edu/ml/index.php.
   Google Scholar
• Verma, B, and Z. S. Hassan. 2011. Hybrid ensemble approach for classification. Applied Intelligence 34 (2):258–78. doi:10.1007/s10489-009-0194-7.
   Web of Science ®Google Scholar
• Wang, R. Y., V. C. Storey, and C. P. Firth. 1995. A framework for analysis of data quality research. IEEE Transactions on Knowledge and Data Engineering 7 (4):623–40. doi:10.1109/69.404034.
   Web of Science ®Google Scholar
• Watson, P. F, and A. Petrie. 2010. Method agreement analysis: A review of correct methodology. Theriogenology 73 (9):1167–79. doi:10.1016/J.THERIOGENOLOGY.2010.01.003.
   PubMed Web of Science ®Google Scholar
• Witten, I. H., E. Frank, and A. Hall. 2011. Data mining: Practical machine learning tools and techniques, Data Mining (Third Edit, Vol. 277). San Francisco, CA, USA: Elsevier. doi:10.1002/1521-3773(20010316)40:6<9823::AID-ANIE9823>3.3.CO;2-C.
   Google Scholar
• Wu, X, and X. Zhu. 2008. Mining with noise knowledge: Error-aware data mining. IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans 38 (4):917–32. doi:10.1109/TSMCA.2008.923034.
   Web of Science ®Google Scholar
• Wu, Z., W. Lin, and Y. Ji. 2018. An integrated ensemble learning model for imbalanced fault diagnostics and prognostics. IEEE Access. 6:8394–402. doi:10.1109/ACCESS.2018.2807121.
   Web of Science ®Google Scholar
• Xu, B., J. Z. Huang, G. Williams, Q. Wang, and Y. Ye. 2012. Classifying very high-dimensional data with random forests built from small subspaces. International Journal of Data Warehousing and Mining 8 (2):44–63. doi:10.4018/jdwm.2012040103.
   Web of Science ®Google Scholar
• Yang, P., J. Y. H. Yang, B. Zhou, and A. Zomaya. 2010. A review of ensemble methods in bioinformatics. Current Bioinformatics 5 (4):296–308. doi:10.2174/157489310794072508.
   Web of Science ®Google Scholar
• Youden, W. J. 1950. Index for rating diagnostic tests. Cancer 3 (1):32–5. doi:10.1002/1097-0142(1950)3:1<32::AID-CNCR2820030106>3.0.CO;2-3.
   PubMed Web of Science ®Google Scholar
• Zhang, Y., B. Liu, J. Cai, and S. Zhang. 2017. Ensemble weighted extreme learning machine for imbalanced data classification based on differential evolution. Neural Computing and Applications 28 (s1):259–67. doi:10.1007/s00521-016-2342-4.
   Google Scholar
• Zhang, Z., Y. Zhao, A. Canes, D. Steinberg, O. Lyashevska, and written on behalf of AME Big-Data Clinical Trial Collaborative Group. 2019. Predictive analytics with gradient boosting in clinical medicine. Annals of Translational Medicine 7 (7):152–9. doi:10.21037/atm.2019.03.29.
   PubMed Web of Science ®Google Scholar
• Zhu, X, and X. Wu. 2004. Class noise vs. attribute noise: A quantitative study. Artificial Intelligence Review 22 (3):177–210. doi:10.1007/s10462-004-0751-8.
   Web of Science ®Google Scholar