Towards a methodology for addressing missingness in datasets, with an application to demographic health datasets

TL;DR

This paper presents a methodology combining synthetic data generation, missing data imputation, and deep learning to effectively address missingness in health datasets, improving predictive accuracy on unseen data.

Contribution

The study introduces a novel approach that integrates Gaussian mixture models and deep learning for synthetic data creation and missing data imputation in demographic health datasets.

Findings

Models trained on synthetic and imputed data achieved 83% accuracy on real unseen data.

DAE-based imputation yielded the lowest log loss, indicating superior performance.

The methodology is adaptable to other contexts beyond health datasets.

Abstract

Missing data is a common concern in health datasets, and its impact on good decision-making processes is well documented. Our study's contribution is a methodology for tackling missing data problems using a combination of synthetic dataset generation, missing data imputation and deep learning methods to resolve missing data challenges. Specifically, we conducted a series of experiments with these objectives; $a)$ generating a realistic synthetic dataset, $b)$ simulating data missingness, $c)$ recovering the missing data, and $d)$ analyzing imputation performance. Our methodology used a gaussian mixture model whose parameters were learned from a cleaned subset of a real demographic and health dataset to generate the synthetic data. We simulated various missingness degrees ranging from $10 \%$, $20 \%$, $30 \%$, and $40\%$ under the missing completely at random scheme MCAR. We used an integrated performance analysis framework involving clustering, classification and direct imputation analysis. Our results show that models trained on synthetic and imputed datasets could make predictions with an accuracy of $83 \%$ and $80 \%$ on $a) $ an unseen real dataset and $b)$ an unseen reserved synthetic test dataset, respectively. Moreover, the models that used the DAE method for imputed yielded the lowest log loss an indication of good performance, even though the accuracy measures were slightly lower. In conclusion, our work demonstrates that using our methodology, one can reverse engineer a solution to resolve missingness on an unseen dataset with missingness. Moreover, though we used a health dataset, our methodology can be utilized in other contexts.