# Fast Imbalanced Classification of Healthcare Data with Missing Values

arXiv: https://arxiv.org/abs/1503.06250 · HTML: https://mlsanity.com/paper/1503.06250/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Mar 2015.

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

- [Optimal Downsampling for Imbalanced Classification with Generalized Linear Models](https://mlsanity.com/paper/2410.08994/) (2024)
- [Coping with Mistreatment in Fair Algorithms](https://mlsanity.com/paper/2102.10750/) (2021)

## Abstract (arXiv)

In medical domain, data features often contain missing values. This can create serious bias in the predictive modeling. Typical standard data mining methods often produce poor performance measures. In this paper, we propose a new method to simultaneously classify large datasets and reduce the effects of missing values. The proposed method is based on a multilevel framework of the cost-sensitive SVM and the expected maximization imputation method for missing values, which relies on iterated regression analyses. We compare classification results of multilevel SVM-based algorithms on public benchmark datasets with imbalanced classes and missing values as well as real data in health applications, and show that our multilevel SVM-based method produces fast, and more accurate and robust classification results.