___

• Abidin, N. Z., Ismail, A. R., & Emran, N. A. (2018). Performance analysis of machine learning algorithms for missing value imputation. International Journal of Advanced Computer Science and Applications, 9(6), 442-447. https://dx.doi.org/10.14569/IJACSA.2018.090660
• Alamoodi, A. H., Zaidan, B. B., Zaidan, A. A., Albahri, O. S., Chen, J., Chyad, M. A., Garfan, S., & Aleesa, A. M. (2021). Machine learning-based ımputation soft computing approach for large missing scale and non-reference data ımputation. Chaos, Solitons & Fractals, 151, 111236. https://doi.org/10.1016/j.chaos.2021.111236
• Allison, P. D. (2009). Missing data, handbook of quantitative methods in psychology (Editor: Roger E. Millsap ve Alberto Maydeu-Olivares), Sage Publications.
• Baraldi, A. N., & Enders, C. K. (2010). An introduction to modern missing data analyses. Journal of School Psychology, 48(1), 5-37. https://doi.org/10.1016/j.jsp.2009.10.001
• Bi, Q., Goodman, K. E., Kaminsky, J., & Lessler, J. (2019). What is machine learning? A primer for the epidemiologist. American Journal of Epidemiology, 188(12), 2222-2239. https://doi.org/10.1093/aje/kwz189
• Brynjolfsson, E., & Mitchell, T. (2017). What can machine learning do? Workforce implications. Science, 358(6370), 1530-1534. https://doi.org/10.1126/science.aap8062
• Dogan, C. D. (2017). Applying bootstrap resampling to compute confidence intervals for various statistics with R. Eurasian Journal of Educational Research, 17(68), 1-18. https://dergipark.org.tr/en/download/article-file/623638
• Doğru, F. Z., Bulut, Y. M., & Arslan, O. (2016). Finite mixtures of matrix variate t-distributions. Gazi University Journal of Science, 29(2), 335-341. https://dergipark.org.tr/tr/download/article-file/225490
• Donders, A. R. T., Van Der Heijden, G. J., Stijnen, T., & Moons, K. G. (2006). A gentle introduction to imputation of missing values. Journal of Clinical Epidemiology, 59(10), 1087-1091. https://doi.org/10.1016/j.jclinepi.2006.01.014
• Durmuş, B., & Güneri, Ö. İ. (2019). Data mining with R: An applied study. International Journal of Computing Sciences Research, 3(3), 201-216. https://doi.org/10.25147/ijcsr.2017.001.1.34
• Durmuş, B., & Güneri, Ö. İ. (2021). A classification study for re-determination of the geographical regions: The case of Turkey. International Journal of Applied Mathematics Electronics and Computers, 9(4), 97-102. https://doi.org/10.18100/ijamec.988273
• Emmanuel, T., Maupong, T., Mpoeleng, D., Semong, T., Mphago, B., & Tabona, O. (2021). A survey on missing data in machine learning. Journal of Big Data, 8(1), 1-37. https://doi.org/10.1186/s40537-021-00516-9
• Enders, C. K. (2022). Applied missing data analysis. Guilford Publications.
• Honaker, J., King, G., & Blackwell, M. (2011). Amelia II: A program for missing data. Journal of Statistical Software, 45(7), 1–47. https://doi.org/10.18637/jss.v045.i07
• Jadhav, A., Pramod, D., & Ramanathan, K. (2019). Comparison of performance of data imputation methods for numeric dataset. Applied Artificial Intelligence, 33(10), 913-933. https://doi.org/10.1080/08839514.2019.1637138
• Jerez, J. M., Molina, I., García-Laencina, P. J., Alba, E., Ribelles, N., Martín, M., & Franco, L. (2010). Missing data imputation using statistical and machine learning methods in a real breast cancer problem. Artificial Intelligence In Medicine, 50(2), 105-115. https://doi.org/10.1016/j.artmed.2010.05.002
• Kenyhercz, M. W., & Passalacqua, N. V. (2016). Missing data imputation methods and their performance with biodistance analyses. Biological Distance Analysis (pp. 181-194). Academic Press. https://doi.org/10.1016/B978-0-12-801966-5.00009-3
• Köse, I. A., & Öztemur, B. (2014). Kayıp veri ele alma yöntemlerinin t-testi ve ANOVA parametreleri üzerine etkisinin incelenmesi. Abant İzzet Baysal Üniversitesi Eğitim Fakültesi Dergisi, 14(1), 400-412. https://dergipark.org.tr/tr/download/article-file/16769
• Mahesh, B.(2019). Machine learning algorithms-A review. International Journal of Science and Research, 9(1), 381-386.
• Oprea, C. (2014). Performance evaluation of the data mining classification methods. Information Society and Sustainable Development, 1(Special Issue), 249-253. https://www.utgjiu.ro/revista/ec/pdf/2014-04.Special/45_Oprea%20Cristina.pdf
• Palanivinayagam, A., & Damaševičius, R. (2023). Effective handling of missing values in datasets for classification using machine learning methods. Information, 14(2), 92. https://doi.org/10.3390/info14020092
• Raja, P. S., & Thangavel, K. J. S. C. (2020). Missing value imputation using unsupervised machine learning techniques. Soft Computing, 24(6), 4361-4392. https://doi.org/10.1007/s00500-019-04199-6
• Schaffer, J. L. (1997). Analysis of incomplete multivariate data. Chapman&Hall.
• Tang, F., & Ishwaran, H. (2017). Random forest missing data algorithms. Statistical Analysis and Data Mining: The ASA Data Science Journal, 10(6), 363-377. https://doi.org/10.1002/sam.11348
• Thomas, T., & Rajabi, E. (2021). A systematic review of machine learning-based missing value ımputation techniques. Data Technologies and Applications, 55(4), 558-585. https://doi.org/10.1108/DTA-12-2020-0298
• Vangipuram, R., Gunupudi, R. K., Puligadda, V. K., & Vinjamuri, J. (2020). A machine learning approach for imputation and anomaly detection in iot environment. Expert Systems, 37(5), e12556. https://doi.org/10.1111/exsy.12556
• Vembandasamy, K., Sasipriya, R., & Deepa, E. (2015). Heart diseases detection using naive bayes algorithm. International Journal of Innovative Science, Engineering & Technology, 2(9), 441-444. https://ijiset.com/vol2/v2s9/IJISET_V2_I9_54.pdf
• Zhang, Z. (2016). Introduction to machine learning: K-nearest neighbors. Annals of Translational Medicine, 4(11), 218-224. https://doi.org/10.21037/atm.2016.03.37