Case-Based Reasoning Model for Predicting the Malaria Cases

Explainable AI for Malaria Diagnosis: Comparative Analysis of ML Models Using Random Forest Feature Selection and SHAP Interpretability

Malaria remains one of the most significant causes of morbidity and mortality in tropical and subtropical regions, and timely diagnosis is essential for effective case management. Microscopy is the traditional parasitological reference standard, while rapid diagnostic tests (RDTs) are widely used, field-deployable alternatives with product- and antigen-dependent sensitivity and specificity; both approaches are constrained by requirements for trained personnel, reagents, or equipment in resource-limited settings. This study develops and evaluates, on a simulated clinical dataset calibrated to published aggregate statistics, an explainable artificial intelligence pipeline for malaria diagnosis prediction from routinely collectable symptoms, vital signs, and haematological indices, using six machine-learning (ML) models: Logistic Regression (LR), Naive Bayes (NB), K-Nearest Neighbours (KNN), Random Forest (RF), Support Vector Classifier (SVC), and Decision Tree (DT). The Synthetic Minority Oversampling Technique (SMOTE) and Random Forest feature selection are embedded within a single leakage-safe pipeline that is refitted in every cross-validation fold. The reported best model is selected on the basis of the cross-validated F1-score rather than held-out test performance, and the test set is used exactly once for confirmatory reporting. Under this design, SVC with RF-selected features was selected (mean cross-validated F1 = 0.740), achieving a test-set accuracy of 0.906 [95% CI 0.852, 0.953], recall of 0.867 [0.667, 1.000], and ROC AUC of 0.959 [0.919, 0.988]. A paired bootstrap test found no statistically significant difference in AUC compared with the runner-up, Logistic Regression (AUC 0.956, p = 0.81). Permutation importance corroborated 8 of the top 10 impurity-based features. Parasite density, the quantity used to determine the parasitological diagnosis, was excluded from the predictor set as a precautionary safeguard against near-total label leakage. Calibration, subgroup recall by age and sex, and a class-weighting comparison are also reported. 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.

Leveraging machine learning for symptom-based malaria diagnosis: a predictive model approach

Explainable machine learning framework for foodborne disease outbreak prediction in Eastern Province, Saudi Arabia: case study

The occurrence of foodborne diseases is a considerable public health issue, especially in areas that are quickly becoming urbanized with intricate food delivery systems. In this paper, we present 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). The final data set includes 61 cases and 13 engineered features. In the current research, the proposed architecture uses XGBoost to predict outbreaks, alongside using the random forest for predicting severity and support vector machine (SVM) for comparisons. The XGBoost classifier demonstrates an evenly balanced performance (accuracy = 0.85, precision = 0.78, recall = 0.78) on the testing set. Due to the size of the dataset, the results are provided with the estimation of uncertainty (rather than the exact numbers). Using leakage-safe repeated stratified cross-validation, the mean AUC equals 0.64 [95% interval = (0.20, 1.00)], and the leave-one-year-out validation method is not stable (mean AUC 0.47). Differences between the models (e.g., better single-split cross-validation AUC for SVM) are within confidence intervals. Interpretability is improved by using the SHAP framework to measure feature importance, which shows that the main factors are hospitalization and the severity of symptoms. The graph module also helps in understanding the propagation of disease risk between cities, highlighting the importance of well-connected metropolitan areas as disease hubs. Moreover, the use of a locally deployed Mistral LLM makes the generated explanations more readable. The findings show that our approach presents an appropriate balance of predictiveness, interpretability, and spatial knowledge. With only 61 data points and 11 outbreaks reported, this research is clearly not meant to be an early warning system, but rather a proof-of-concept on how one might be designed. In order to ensure reproducibility, the preprocessing pipeline and synthetic dataset generator have been made available to the community.

Geographically Weighted Machine Learning Model for Untangling Spatial Heterogeneity of Dengue Incidence in West Java

Dengue hemorrhagic fever continues to pose a significant public health challenge, particularly in West Java Province, Indonesia, which consistently reports the highest incidence rates in the country. This study examined the factors influencing dengue fever incidence using Random Forest Regression (RFR) and Geographically Weighted Random Forest (GWRF) methodologies. Utilizing secondary data from 2022 to 2024 across 27 districts/cities, the data from 2022 to 2023 served as training data, while the 2024 data were used for testing. The findings revealed that the optimal RFR model, with ntree = 1000 and mtry = 1, achieved an RMSE of 1796.409, a MAPE of 0.482, and an  of 0.685. Conversely, the GWRF model, which employed an adaptive kernel and an optimal bandwidth of 45 nearest neighbors, exhibited superior performance, with an RMSE of 1,756.713, MAPE of 0.466, and  of 0.700. This enhancement in the model performance suggests that spatial weighting improves the model's capacity to capture spatial heterogeneity. In addition, variations in local feature importance indicate spatial non-stationarity across regions. These results imply that the GWRF is more effective in modeling dengue fever outbreaks and can inform the development of region-specific public health interventions.

Comparative evaluation of machine learning strategies for short-term dengue forecasting in Brazilian capital municipalities

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.

Use of Geospatial Big Data Intelligence Software for Epidemic Forecasting and Public Health Decision-Making:

Background Surveillance of infectious diseases remains a persistent challenge in many countries across the world. Most of these countries have slow reporting systems, poor infrastructure of data, and a limited capability of predictive analytics. Conventional surveillance methods are reactive, which leads to delayed outbreak containment. 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. Methods A longitudinal dataset comprising 152,438 observations (2020-2024) across 30 LGAs in six states was analyzed. The confirmed figures of COVID-19, Cholera, Lassa Fever, Malaria and Ebola, mobility index, temperature, rainfall, population density and location data. The study utilized Logistic Regression (LR), Random Forest (RF), Gradient Boosting Machine (GBM), and ARIMA. We evaluated the performance of our model based on Accuracy, Precision and Recall. A module for the detection of anomalies. Results According to the results, Random Forest had the highest predictive performance (AUC = 0.94; F1-score = 0.91) followed by Gradient Boosting (AUC = 0.92; F1-score = 0.89).  Moderate performance (AUC = 0.84) by Logistic Regression. ARIMA was able to capture trends over time but considerably underperformed at outbreak spikes (RMSE = 18.7). Mobility index and rainfall were necessary predictors (p < 0.05). Detection framework identified outbreak spikes with 93% sensitivity. Conclusion Machine learning early warning systems outperform conventional surveillance systems in developing countries. The fusion of environmental and mobility variables helps in prediction and containment at source. The framework provides a huge scalable and efficient resource model for digital disease surveillance in countries