# Enhancing cardiotocography classification via ensemble learning and threshold optimization

## TL;DR

This paper introduces a new method to improve the classification of cardiotocography data using machine learning techniques, leading to better detection of critical cases.

## Contribution

The novel multifusion method combines undersampling, threshold-moving optimization, and ensemble classifiers to address class imbalance in CTG data.

## Key findings

- The proposed method significantly improved the precision of identifying pathological cases in the CTG dataset.
- Baseline models correctly classified only about 2 out of 11 cases, while the new approach achieved up to 76.92% precision.
- The method accurately identified 9 out of 12 cases in one of the test categories.

## Abstract

Machine learning classifiers trained on imbalanced healthcare datasets often exhibit bias, leading to poor performance on critical cases. The cardiotocography (CTG) dataset exemplifies this issue, where misclassification of pathological cases arises due to both class imbalance and non-optimal probability thresholds. Statistical analysis suggests refining classification thresholds, but this approach has been largely overlooked in CTG data research. To address these challenges, we propose a multifusion method integrating undersampling, threshold-moving optimization, and ensemble classifiers to enhance classification precision while maintaining computational efficiency. Applied to a CTG dataset of 502 cases from Czech Technical University and University Hospital Brno, our method showed significant improvements in identifying pathological cases. While baseline models correctly classified only about 2 out of 11 cases per test, our approach achieved 76.92, 75, and 41.67% precision, accurately identifying 9, 9, and 3 cases out of 12, respectively.