Early Fault Detection in a Real Scenario of Hybrid Fiber–Coaxial Networks Using Machine Learning: An Approach Based on Decision Trees and Random Forests
Christian Szcerba, Enrique Dávalos, Ariel Leiva, Juan Pinto-Ríos · Applied Sciences · 2025
Cable service providers face significant challenges in managing Hybrid Fiber–Coaxial (HFC) networks due to the growing demand for high-speed services. Ensuring high service availability is critical to preventing customer attrition. This study employs machine learning techniques, specifically Decision Tree and Random Forest models, for proactive fault detection in HFC networks using data from the Simple Network Management Protocol (SNMP). Two operational scenarios were considered: a network-wide model and node-specific models. The dataset for fault detection exhibited a severe class imbalance, with outage events being extremely rare. To address this, the Synthetic Minority Oversampling Technique (SMOTE), which generates synthetic samples of the minority class to balance the dataset, was applied. This significantly improved recall and F1-scores—the harmonic mean of precision and recall—while maintaining high precision. The results demonstrate that these machine learning algorithms achieve up to 98% accuracy, and the SMOTE-enhanced models provide more reliable detection of connectivity faults. This approach is highly effective for cable operators in maintaining quality of service, enabling proactive management of problems and enhancement of network performance.