  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.
