Diagnosis of NEC using a Multi-Feature Fusion Machine Learning Algorithm

Abstract

Necrotizing enterocolitis (NEC) is a severe gastrointestinal emergency in neonates, marked by its complex etiology, ambiguous clinical manifestations, and significant morbidity and mortality, profoundly affecting long-term pediatric health outcomes. The prevailing diagnostic approaches for NEC, including traditional manual auscultation of bowel sounds, suffer from limited sensitivity and specificity, leading to potential misdiagnoses and delayed treatment. In this paper, we introduce a groundbreaking NEC diagnostic framework employing machine learning algorithms that utilize multi-feature fusion of bowel sounds, significantly improving the diagnostic accuracy. Bowel sounds from NEC patients and healthy newborns are meticulously captured using a specialized acquisition system, designed to overcome the inherent challenges associated with the low amplitude, substantial background noise, and high variability of neonatal bowel sounds. To enhance the diagnostic framework, we extract mel-frequency cepstral coefficient (MFCC), short-time energy (STE), and zero-crossing rate (ZCR) to capture comprehensive frequency and time domain features, ensuring a robust representation of bowel sound characteristics. These features are then integrated using a multi-feature fusion technique to form a singular feature vector, providing a rich, integrated dataset for the machine learning algorithm. Employing the support vector machine (SVM), the algorithm achieved an accuracy (ACC) of 88.00%, sensitivity (SEN) of 100.00%, and an area under the receiver operating characteristic (ROC) curve (AUC) of 97.62%, achieving high accuracy in diagnosing NEC. This innovative approach not only improves the accuracy and objectivity of NEC diagnosis but also shows promise in revolutionizing neonatal care through facilitating early and precise diagnosis. It significantly enhances clinical outcomes for affected neonates.