Assessing Performance Across Various Machine Learning Algorithms with Integrated Feature Selection for Fetal Heart Classification - Amanda, Laura Rizka; Anasanti, Mila Desi (International Journal of Artificial Intelligence Research, 2024)
The global concern over declining perinatal death rates, particularly in low- and middle-income nations, underscores the importance of adopting Cardiotocography (CTG) as a vital fetal monitoring method. Recent strides in machine learning (ML) present promising opportunities to enhance the accuracy of assessing fetal health, providing a viable alternative to traditional approaches. This study aims to evaluate various ML methodologies and feature selection techniques for predicting fetal health using CTG data. The primary objective is to improve ML algorithms' accuracy, precision, recall, and F1 score while selecting the most critical features. The dataset includes 2,126 expectant mothers in the third trimester, with 35 variables related to fetal heart rate (FHR) and uterine contractions (UC). Preprocessing involves feature scaling, data balancing, and outlier elimination. Additionally, a 10-fold stratified cross-validation approach is employed to ensure robust evaluation and generalizability of the model's performance. Six ML algorithms—Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), Naive Bayes (NB), Logistic Regression (LR), and K-Nearest Neighbors (KNN)—are employed, optimized through grid search cross-validation. The RF algorithm outperforms with an impressive 99% accuracy, closely followed by DT at 98.7%. Optimizing 15 features from the original 35 using Simultaneous Perturbation Feature Selection and Ranking (spFSR) yields a remarkable accuracy of 99%, mirroring the full feature set. This underscores the vital role of selected features in improving predictive power and overall model performance. The study emphasizes the efficacy of tree-based classification algorithms, especially RF, in predicting fetal health and highlights the impact of preprocessing on model performance. These findings suggest avenues for future research, including exploring alternative feature engineering methods and assessing algorithm performance in diverse scenarios.