Case-Based Reasoning Model for Predicting the Malaria Cases
TL;DR
Beyond predictive accuracy, qualitative criteria including explain-ability, transparency, and adaptability were evaluated, further highlighting the superiority of CBR 2 over conventional black-box models.
Abstract
Malaria remains a major public health issue in Ivory Coast, where the need for accurate and interpretable predictive models is critical for effective disease control. While most existing approaches prioritize predictive accuracy over interpretability, this study addresses the need for explainable models suitable for deployment in resource-limited public health settings. The study used a dataset covering multiple regions of Cˆote d’Ivoire over the period 2019–2023 and including climatic variables such as temperature, humidity, and precipitation. Seven conventional machine learning models (CatBoost, XGBR, RFR, DTR, SVM, KNN, and Linear Regression) were compared with three proposed Case-Based Reasoning variants (CBR 0, CBR 1, and CBR 2), which differ in their similarity-weighting strategies and correction constant. The results show that CBR 2 achieved the best predictive performance, with RMSE = 0.081, MAE = 0.057, and R2 = 72.40%, followed by the Random Forest Regressor. A Wilcoxon signed-rank test confirmed a statistical significant difference of this permofance (W = 18029, p = 1.02 × 10−20). Beyond predictive accuracy, qualitative criteria including explain-ability, transparency, and adaptability were evaluated, further highlighting the superiority of CBR 2 over conventional black-box models. These findings highlight the potential of Case-Based Reasoning for epidemiological forecasting and decision support in malaria control.