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Mammography is one of the most effective methods for breast cancer screening and the resulting images are normally reported using the BI-RADS standard. Missing values are found in this BI-RADS dataset which can reduce the classification performance of any machine learning algorithm. This study applies a few established imputation methods that estimate and replace the missing values found in a mammogram mass dataset. Then, a few machine learning algorithms learnt from these imputed datasets to classify between benign and malignant masses. Using classification accuracy as the performance metric, the experimental results showed an increase in accuracy for majority of the combination of machine learning algorithms algorithm and imputation methods. Keyword—Imputation, machine learning, mammography, missing values.

Imputing Missing Values in Mammography Mass Dataset: Will it Increase Classification Performance of Machine Learning Algorithms?