CFCM-SMOTE: A Robust Fetal Health Classification to Improve Precision Modeling in Multiclass Scenarios

Abstract

The advent of cardiotocography (CTG) has radically transformed prenatal care, facilitating in-depth evaluations of fetal health. Despite this, the reliability of CTG is frequently undermined by data-related issues, such as outliers and class imbalanced data. To address these challenges, our study introduces an innovative integrated methodology that combines cluster-based fuzzy C-means (CFCM) with the synthetic minority oversampling technique (SMOTE) to improve the precision of classification of fetal health status classification in multiclass scenarios. We used a considerable dataset from the UCI Machine Learning Repository, employing CFCM to manage outliers and SMOTE to rectify class imbalanced data. This approach has significantly improved the performance of the classification algorithm, a fact that is corroborated by the comprehensive experimental validation that can be found in the study in Ref. [1]. We observed notable improvements in several evaluation metrics, including precision (PRC), sensitivity (SNS), specificity (SPC), F1 score (F1-S), and accuracy (ACC), surpassing the capabilities of prior methodologies. Specifically, the deployment of our algorithm amplified the precision (PRC: from 98.16% to 99.58%), sensitivity (SNS: from 95.82% to 100%), specificity (SPC: from 85.81% to 99.75%), F1 score (F1-Score: from 96.98% to 99.79%), and accuracy (ACC: from 94.20% to 99.84%) of the Classification and Regression Tree (CART) algorithm for the 'normal' class, while also improving the precision and accuracy of the Random Forest (RF) algorithm from PRC: 94.77% to 95.89% and ACC: 90.60% to 97.45%. These results confirm the potential of CFCM-SMOTE as a robust model for fetal health diagnostics and as a basic strategy for the development of predictive analyzes in prenatal healthcare.