2026· International Journal of Advanced Computer Science and Applications· 0 citations· 36 references
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.
This framework illustrates, without any claim of clinical validity, how a leakage-safe ML pipeline and SHAP interpretability can be combined and rigorously self-audited; real patient-level data and external validation are required before any clinical inference is drawn.
David Chepkonga, A. Langat, Ebenezer Esenogho et al.· Asian Journal of Research in...· 0 citations
A region-specific malaria prediction model for clinical diagnosis that uses patient demographics and clinical symptoms has the potential to assist in the development of a clinically based malaria diagnostic system.
S. Ruban, V. Vinay, Hilda Shanthini et al.· Neural computing & applicati...· 0 citations
A machine learning-based model for predicting outbreaks, explainability, and spatial risk propagation, validated through a multiyear data set of an epidemiological nature from 12 cities in the Eastern Province of Saudi Arabia (2021–2025).
N. F. Saleem ALAnsary, Mahmood Berekaa, Raghad Alhotheyfa et al.· Frontiers in Public Health· 0 citations
The results imply that the GWRF is more effective in modeling dengue fever outbreaks and can inform the development of region-specific public health interventions.
Dengue is an arboviral disease of high public health relevance, characterized by pronounced temporal variability, nonlinearity, and recurrent outbreaks, which pose challenges to epidemiological surveillance and decision-making. This study evaluated the performance of machine learning methods for short-term forecasting of the weekly dengue morbidity rate in the 27 Brazilian capital cities, comprising the 26 state capitals and Brasília, Federal District, with horizons up to 4 weeks. Epidemiological, climatic, and socioeconomic data were compiled for these capital cities and used to compare a Gated Recurrent Unit (GRU) neural network, formulated as a Multi-Input Multi-Output (MIMO) model, and a Gradient Boosting model (CatBoost), implemented using a Direct forecasting strategy with horizon-specific models. Validation was conducted using a walk-forward approach, with evaluation based on absolute error metrics and the coefficient of determination. The results indicated that the GRU architecture presented recurring limitations, including underfitting, temporal lag, and low capacity to anticipate epidemic peaks. In contrast, the CatBoost model demonstrated greater robustness and better adaptation to the variability of epidemiological time series, showing superior performance in most of the analyzed capitals. The findings reinforce that greater architectural complexity does not necessarily imply better operational performance and highlight the potential of ensemble-based methods for short-term epidemiological surveillance applications. These findings contribute to dengue forecasting by showing that, under a common validation framework, ensemble-based strategies may provide greater operational robustness than recurrent MIMO architectures for short-term prediction in heterogeneous epidemiological settings.
D. C. da Cunha e Silva, L. M. Nery, Nícholas de Paula Nicomedes et al.· International journal of bio...· 0 citations
The development and assessment of a machine learning-driven early warning system for infectious disease prediction using geospatial big data from South-Western Nigeria, at the level of Local Government Area outperform conventional surveillance systems in developing countries.
I. Adewumi, N. Bakare, W. Ajayi et al.· London Journal of Physics· 0 citations