# Comparison of Missing Data Imputation Methods using the Framingham Heart study dataset

arXiv: https://arxiv.org/abs/2210.03154 · HTML: https://mlsanity.com/paper/2210.03154/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 22 references that are in the map.
- First posted on arXiv 6 Oct 2022.

## Builds on (most cited first, 3 of 3)

- [GAIN: Missing Data Imputation using Generative Adversarial Nets](https://mlsanity.com/paper/1806.02920/) (2018)
- [Obesity Prediction with EHR Data: A deep learning approach with interpretable elements](https://mlsanity.com/paper/1912.02655/) (2019)
- [Machine Learning-Based Classification Algorithms for the Prediction of Coronary Heart Diseases](https://mlsanity.com/paper/2112.01503/) (2021)

## Built on by (most cited first, 2 of 2)

- [Handling missing values in healthcare data: A systematic review of deep learning-based imputation techniques](https://mlsanity.com/paper/2210.08258/) (2022)
- [M-DEW: Extending Dynamic Ensemble Weighting to Handle Missing Values](https://mlsanity.com/paper/2405.00182/) (2024)

## Abstract (arXiv)

Cardiovascular disease (CVD) is a class of diseases that involve the heart or blood vessels and according to World Health Organization is the leading cause of death worldwide. EHR data regarding this case, as well as medical cases in general, contain missing values very frequently. The percentage of missingness may vary and is linked with instrument errors, manual data entry procedures, etc. Even though the missing rate is usually significant, in many cases the missing value imputation part is handled poorly either with case-deletion or with simple statistical approaches such as mode and median imputation. These methods are known to introduce significant bias, since they do not account for the relationships between the dataset's variables. Within the medical framework, many datasets consist of lab tests or patient medical tests, where these relationships are present and strong. To address these limitations, in this paper we test and modify state-of-the-art missing value imputation methods based on Generative Adversarial Networks (GANs) and Autoencoders. The evaluation is accomplished for both the tasks of data imputation and post-imputation prediction. Regarding the imputation task, we achieve improvements of 0.20, 7.00% in normalised Root Mean Squared Error (RMSE) and Area Under the Receiver Operating Characteristic Curve (AUROC) respectively. In terms of the post-imputation prediction task, our models outperform the standard approaches by 2.50% in F1-score.