Attention-Based LSTM Model for Malaria Severity Prediction in Bayelsa State using Clinical, Environmental and Geospatial Data

Abstract

Malaria remains a critical public health concern in Nigeria, with Bayelsa State experiencing persistent transmission due to its tropical climate, riverine geography, and seasonal flooding. Early identification of malaria severity is essential for effective clinical management, reduction of complications, and optimal allocation of limited healthcare resources. This study presents an attention-based Long Short-Term Memory (LSTM) deep learning model for predicting malaria severity levels, categorized as low, moderate, and high using integrated clinical, environmental, temporal, and geospatial data collected within Bayelsa State. The dataset comprised patient demographic attributes, clinical indicators such as body temperature, environmental variables including rainfall and climate temperature, and geospatial information at the local government area (LGA) level. Rigorous data preprocessing, feature engineering, and data leakage prevention techniques were employed to enhance model reliability. Class imbalance was addressed using the Synthetic Minority Over sampling Technique (SMOTE) and class-weighted training. Experimental evaluation using multiple performance metrics demonstrated that the proposed attention-based LSTM model achieved strong and balanced predictive performance across all severity classes. The results underscore the effectiveness of deep learning with attention mechanisms for malaria severity prediction and highlight its potential application as a clinical decision support tool in malaria-endemic regions such as Bayelsa State.