Multilevel Weighted Support Vector Machine for Classification on Healthcare Data with Missing Values

TL;DR

This paper introduces a multilevel weighted SVM approach tailored for healthcare data with missing values, improving classification accuracy and robustness in the presence of noisy, imbalanced, and incomplete data.

Contribution

It presents a novel multilevel framework combining cost-sensitive SVM and imputation via expected maximization, specifically designed for healthcare datasets with missing entries.

Findings

Outperforms standard methods on benchmark datasets with missing data.

Achieves faster and more accurate classification in healthcare applications.

Demonstrates robustness against data imbalance and noise.

Abstract

This work is motivated by the needs of predictive analytics on healthcare data as represented by Electronic Medical Records. Such data is invariably problematic: noisy, with missing entries, with imbalance in classes of interests, leading to serious bias in predictive modeling. Since standard data mining methods often produce poor performance measures, we argue for development of specialized techniques of data-preprocessing and classification. In this paper, we propose a new method to simultaneously classify large datasets and reduce the effects of missing values. It 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.