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Research Article | DOI: https://doi.org/10.31579/2642-9756/118
1 Department of Natural Science, State University of Zanzibar, Zanzibar-Tanzania
2 Department of Statistics, University of Dar es Salaam, Dar es Salaam-Tanzania
*Corresponding Author: Rahibu A. Department of Natural Science, State University of Zanzibar, Zanzibar-Tanzania.
Citation: Rahibu A. Abassi, Amina S. Msengwa, Rocky R. J. Akarro. (2022). Imputation Methods on Retrospective Breast Cancer Data in Tanzania: A Comparative Study. J. Women Health Care and Issues. 5(4): DOI: 10.31579/2642-9756/118.
Copyright: © 2022 Rahibu A. Abassi. This is an open-access article distributed under the terms of The Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Received: 18 April 2022 | Accepted: 25 May 2022 | Published: 06 June 2022
Keywords: breast cancer dataset; classification methods; imputation methods; missing data
Background: Clinical datasets are at risk of having missing data for several reasons including patients’ failure to attend clinical measurements and measurement recorder’s defects. Missing data can significantly affect the analysis and results might be doubtful due to bias caused by omission incomplete records during analysis especially if a dataset is small. This study aims to compare several imputation methods in terms of efficiency in filling-in missing data so as to increase prediction and classification accuracy in breast cancer dataset.
Methodology: Five imputation methods namely series mean, k-nearest neighbour, hot deck, predictive mean matching, expected maximisation via bootstrapping, and multiple imputation by chained equations were applied to replace the missing values to the real breast cancer dataset. The efficiency of imputation methods was compared by using the Root Mean Square Errors and Mean Absolute Errors to obtain a suitable complete dataset. Binary logistic regression and linear discrimination classifiers were applied to the imputed dataset to compare their efficacy on classification and discrimination.
Results: The evaluation of imputation methods revealed that the predictive mean matching method was better off compared to other imputation methods. In addition, the binary logistic regression and linear discriminant analyses yield almost similar values on overall classification rates, sensitivity and specificity.
Conclusion: The predictive mean matching imputation showed higher accuracy in estimating and replacing missing data values in a real breast cancer dataset under the study. It is a more effective and good approach to handle missing data. We recommend replacing missing data by using predictive mean matching since it is a plausible approach toward multiple imputations for numerical variables. It improves estimation and prediction accuracy over the use complete-case analysis especially when percentage of missing data is not very small.
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