Detecting Lassa Fever Using Ant Lion Optimization with SMOTE and Edited Nearest Neighbours

Lassa fever remains a significant public health concern in West Africa, with an estimated 100,000 to 300,000 cases and over 5,000 deaths annually. Early detection is hindered by diagnostic delays, symptom overlap with other febrile illnesses, and insufficient diagnostic infrastructure. The effectiveness and generalizability of existing Lassa fever prediction models are limited by class imbalance, feature redundancy, and suboptimal configurations, despite the promise of machine learning for disease identification. This study introduces a hybrid framework integrating Edited Nearest Neighbors (ENN) for noise reduction, Synthetic Minority Oversampling Technique (SMOTE) for class balancing, and Ant Lion Optimization (ALO) for feature selection. The framework was evaluated using 20,062 clinical records with 99 features from Nigeria's disease surveillance system collected between 2017 and 2022. When combined with Random Forest classification, the ALO+SMOTE+ENN approach achieved 100% accuracy, precision, recall, F1-score, and an AUC of 1.00. In contrast, conventional methods such as Logistic Regression, Support Vector Machine (SVM), LightGBM, and Gradient Boosting attained only 75–76% accuracy and exhibited notable precision-recall trade-offs, indicating a 24–25% improvement with the proposed method. The superior performance is attributed to Random Forest's robust learning on preprocessed data, ALO's comprehensive feature selection, and SMOTEENN's effective management of the 3:1 class imbalance and noise. This approach reduces diagnostic uncertainty while preserving computational efficiency and clinical interpretability, supporting reliable automated diagnosis in resource-limited healthcare environments. The findings underscore that improving data quality through advanced preprocessing and metaheuristic optimization yields superior results compared to applying complex algorithms to imbalanced datasets, with significant implications for AI-driven infectious disease surveillance in sub-Saharan Africa.