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Missing data imputation in breast cancer prognosis

Abstract:

Missing data are often a problem present in real datasets and different imputation techniques are normally used to alleviate this problem. In this paper we analyze the performance of two different data imputation methods in a task where the aim is to pbkp_redict the probability of breast cancer relapse. Mean imputation and hot-deck methods were used to replace missing values found in a dataset containing 3679 records of patients. Artificial neural network models were trained with the standard dataset (containing no missing data but a restricted number of cases) and also with the data reconstructed by using the two imputation methods mentioned above. The results were analyzed in terms of the pbkp_redictive accuracy and also in terms of the calibration of the results.