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

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.