CUSTOMER CHURN PREDICTION IN MOBILE NETWORKS: A GA-OPTIMIZED K-MEANS AND NEURAL NETWORK APPROACH - Jibril, Bello; Obunadike, G. N; Nuradeen, S. A (FUDMA Journal of Sciences, 2026)
Customer churn prediction constitutes a critical computational challenge within Nigeria's telecommunications ecosystem, characterized by substantial economic ramifications exceeding ₦150 billion in annual revenue attrition. This investigation presents a novel algorithmic synthesis integrating Genetic Algorithm optimization protocols, K-means clustering methodologies, and Artificial Neural Network architectures, specifically calibrated to address the distinctive behavioral and infrastructural characteristics inherent within Nigeria's telecommunications marketplace. Empirical validation utilizing comprehensive multi-operator telecommunications datasets (n=17,743) demonstrates that the proposed GA-K-means-ANN computational framework achieves superior predictive performance with 87.3% accuracy and 83.4% F1-score metrics, representing statistically significant improvements of 11.1% relative to baseline methodological approaches (p
Keywords: Customer churn prediction; Genetic Algorithm optimization; K-means clustering; Artificial Neural Networks; Nigerian telecommunications infrastructure