Classification Methods Based on Machine Learning for the Analysis of Fetal Health Data

TL;DR

This paper evaluates various machine learning models, including SVM, RF, and TabNet, for fetal health classification, demonstrating high accuracy and the importance of feature reduction techniques to improve diagnostic precision.

Contribution

It compares multiple ML models and dimensionality reduction methods for fetal health analysis, highlighting TabNet's high accuracy and interpretability.

Findings

TabNet achieved 94.36% accuracy on fetal health data.

Dimensionality reduction improved classification performance.

Machine learning enhances fetal health diagnostics.

Abstract

The persistent battle to decrease childhood mortality serves as a commonly employed benchmark for gauging advancements in the field of medicine. Globally, the under-5 mortality rate stands at approximately 5 million, with a significant portion of these deaths being avoidable. Given the significance of this problem, Machine learning-based techniques have emerged as a prominent tool for assessing fetal health. In this work, we have analyzed the classification performance of various machine learning models for fetal health analysis. Classification performance of various machine learning models, such as support vector machine (SVM), random forest(RF), and attentive interpretable tabular learning (TabNet) have been assessed on fetal health. Moreover, dimensionality reduction techniques, such as Principal component analysis (PCA) and Linear discriminant analysis (LDA) have been implemented to obtain better classification performance with less number of features. A TabNet model on a fetal health dataset provides a classification accuracy of 94.36%. In general, this technology empowers doctors and healthcare experts to achieve precise fetal health classification and identify the most influential features in the process.