Classification Methods Based on Machine Learning for the Analysis of Fetal Health Data
This work addresses fetal health classification to help reduce childhood mortality, but it is incremental as it applies existing methods to a specific medical dataset.
The study evaluated machine learning models, including SVM, RF, and TabNet, for classifying fetal health data, achieving 94.36% accuracy with TabNet and using dimensionality reduction techniques like PCA and LDA to improve performance with fewer features.
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.