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The persistent battle to decrease childhood mortality serves as a commonlyemployed benchmark for gauging advancements in the field of medicine. Globally,the under-5 mortality rate stands at approximately 5 million, with asignificant portion of these deaths being avoidable. Given the significance ofthis problem, Machine learning-based techniques have emerged as a prominenttool for assessing fetal health. In this work, we have analyzed theclassification performance of various machine learning models for fetal healthanalysis. Classification performance of various machine learning models, suchas support vector machine (SVM), random forest(RF), and attentive interpretabletabular 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 betterclassification performance with less number of features. A TabNet model on afetal health dataset provides a classification accuracy of 94.36%. In general,this technology empowers doctors and healthcare experts to achieve precisefetal health classification and identify the most influential features in theprocess.

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