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Collecting sufficient labelled training data for health and medical problemsis difficult (Antropova, et al., 2018). Also, missing values are unavoidable inhealth and medical datasets and tackling the problem arising from theinadequate instances and missingness is not straightforward (Snell, et al.2017, Sterne, et al. 2009). However, machine learning algorithms have achievedsignificant success in many real-world healthcare problems, such as regressionand classification and these techniques could possibly be a way to resolve theissues.

Transfer learning to enhance amenorrhea status prediction in cancer and fertility data with missing values